@article{ZHANG2021100025,
title = {AI technologies for education: Recent research and future directions},
journal = {Computers and Education: Artificial Intelligence},
volume = {2},
pages = {100025},
language = {English},
year = {2021},
issn = {2666-920X},
type = {article},
doi = {https://doi.org/10.1016/j.caeai.2021.100025},
url = {https://www.sciencedirect.com/science/article/pii/S2666920X21000199},
author = {Ke Zhang. and Ayse Begum Aslan},
address={USA},
affiliation={Wayne State University; Eastern Michigan University},
keywords = {Artificial intelligence, AI, AI in Education},
abstract = {From unique educational perspectives, this article reports a comprehensive review of selected empirical studies on artificial intelligence in education (AIEd) published in 1993–2020, as collected in the Web of Sciences database and selected AIEd-specialized journals. A total of 40 empirical studies met all selection criteria, and were fully reviewed using multiple methods, including selected bibliometrics, content analysis and categorical meta-trends analysis. This article reports the current state of AIEd research, highlights selected AIEd technologies and applications, reviews their proven and potential benefits for education, bridges the gaps between AI technological innovations and their educational applications, and generates practical examples and inspirations for both technological experts that create AIEd technologies and educators who spearhead AI innovations in education. It also provides rich discussions on practical implications and future research directions from multiple perspectives. The advancement of AIEd calls for critical initiatives to address AI ethics and privacy concerns, and requires interdisciplinary and transdisciplinary collaborations in large-scaled, longitudinal research and development efforts.}
}

@article{PETROVIC201617,
title = {Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning},
journal = {Artificial Intelligence in Medicine},
volume = {68},
pages = {17-28},
year = {2016},
language = {English},
issn = {0933-3657},
type = {article},
doi = {https://doi.org/10.1016/j.artmed.2016.01.006},
url = {https://www.sciencedirect.com/science/article/pii/S093336571630015X},
author = {Sanja Petrovic and Gulmira Khussainova and Rupa Jagannathan},
affiliation={Nottingham University},
address={UK},
keywords = {Case-based reasoning, Adaptation-guided retrieval, Machine-learning tools, Radiotherapy treatment planning},
abstract = {Objective
Radiotherapy treatment planning aims at delivering a sufficient radiation dose to cancerous tumour cells while sparing healthy organs in the tumour-surrounding area. It is a time-consuming trial-and-error process that requires the expertise of a group of medical experts including oncologists and medical physicists and can take from 2 to 3h to a few days. Our objective is to improve the performance of our previously built case-based reasoning (CBR) system for brain tumour radiotherapy treatment planning. In this system, a treatment plan for a new patient is retrieved from a case base containing patient cases treated in the past and their treatment plans. However, this system does not perform any adaptation, which is needed to account for any difference between the new and retrieved cases. Generally, the adaptation phase is considered to be intrinsically knowledge-intensive and domain-dependent. Therefore, an adaptation often requires a large amount of domain-specific knowledge, which can be difficult to acquire and often is not readily available. In this study, we investigate approaches to adaptation that do not require much domain knowledge, referred to as knowledge-light adaptation.
Methodology
We developed two adaptation approaches: adaptation based on machine-learning tools and adaptation-guided retrieval. They were used to adapt the beam number and beam angles suggested in the retrieved case. Two machine-learning tools, neural networks and naive Bayes classifier, were used in the adaptation to learn how the difference in attribute values between the retrieved and new cases affects the output of these two cases. The adaptation-guided retrieval takes into consideration not only the similarity between the new and retrieved cases, but also how to adapt the retrieved case.
Results
The research was carried out in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. All experiments were performed using real-world brain cancer patient cases treated with three-dimensional (3D)-conformal radiotherapy. Neural networks-based adaptation improved the success rate of the CBR system with no adaptation by 12%. However, naive Bayes classifier did not improve the current retrieval results as it did not consider the interplay among attributes. The adaptation-guided retrieval of the case for beam number improved the success rate of the CBR system by 29%. However, it did not demonstrate good performance for the beam angle adaptation. Its success rate was 29% versus 39% when no adaptation was performed.
Conclusions
The obtained empirical results demonstrate that the proposed adaptation methods improve the performance of the existing CBR system in recommending the number of beams to use. However, we also conclude that to be effective, the proposed adaptation of beam angles requires a large number of relevant cases in the case base.}
}

@article{ROLDANREYES20151,
title = {Improvement of online adaptation knowledge acquisition and reuse in case-based reasoning: Application to process engineering design},
journal = {Engineering Applications of Artificial Intelligence},
volume = {41},
pages = {1-16},
affiliation={Université de Toulouse; Instituto Tecnologico de Orizaba},
country={France},
language = {English},
year = {2015},
type = {article},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2015.01.015},
url = {https://www.sciencedirect.com/science/article/pii/S0952197615000263},
author = {E. {Roldan Reyes} and S. Negny and G. {Cortes Robles} and J.M. {Le Lann}},
keywords = {Case based reasoning, Constraint satisfaction problems, Interactive adaptation method, Online knowledge acquisition, Failure diagnosis and repair},
abstract = {Despite various publications in the area during the last few years, the adaptation step is still a crucial phase for a relevant and reasonable Case Based Reasoning system. Furthermore, the online acquisition of the new adaptation knowledge is of particular interest as it enables the progressive improvement of the system while reducing the knowledge engineering effort without constraints for the expert. Therefore this paper presents a new interactive method for adaptation knowledge elicitation, acquisition and reuse, thanks to a modification of the traditional CBR cycle. Moreover to improve adaptation knowledge reuse, a test procedure is also implemented to help the user in the adaptation step and its diagnosis during adaptation failure. A study on the quality and usefulness of the new knowledge acquired is also driven. As our Knowledge Based Systems (KBS) is more focused on preliminary design, and more particularly in the field of process engineering, we need to unify in the same method two types of knowledge: contextual and general. To realize this, this article proposes the integration of the Constraint Satisfaction Problem (based on general knowledge) approach into the Case Based Reasoning (based on contextual knowledge) process to improve the case representation and the adaptation of past experiences. To highlight its capability, the proposed approach is illustrated through a case study dedicated to the design of an industrial mixing device.}
}

@article{JUNG20095695,
title = {Integrating radial basis function networks with case-based reasoning for product design},
journal = {Expert Systems with Applications},
volume = {36},
number = {3, Part 1},
language = {English},
pages = {5695-5701},
year = {2009},
type = {article},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2008.06.099},
url = {https://www.sciencedirect.com/science/article/pii/S0957417408003667},
author = {Sabum Jung and Taesoo Lim and Dongsoo Kim},
affiliation={LG Production Engineering Research Institute; Sungkyul University; Soongsil University},
keywords = {Case-based reasoning (CBR), Radial basis function network (RBFN), Design expert system, Product design},
abstract = {This paper presents a case-based design expert system that automatically determines the design values of a product. We focus on the design problem of a shadow mask which is a core component of monitors in the electronics industry. In case-based reasoning (CBR), it is important to retrieve similar cases and adapt them to meet design specifications exactly. Notably, difficulties in automating the adaptation process have prevented designers from being able to use design expert systems easily and efficiently. In this paper, we present a hybrid approach combining CBR and artificial neural networks in order to solve the problems occurring during the adaptation process. We first constructed a radial basis function network (RBFN) composed of representative cases created by K-means clustering. Then, the representative case most similar to the current problem was adjusted using the network. The rationale behind the proposed approach is discussed, and experimental results acquired from real shadow mask design are presented. Using the design expert system, designers can reduce design time and errors and enhance the total quality of design. Furthermore, the expert system facilitates effective sharing of design knowledge among designers.}
}

@article{CHIU2023100118,
title = {Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education},
journal = {Computers and Education: Artificial Intelligence},
volume = {4},
language = {English},
type = {article},
pages = {100118},
year = {2023},
issn = {2666-920X},
doi = {https://doi.org/10.1016/j.caeai.2022.100118},
url = {https://www.sciencedirect.com/science/article/pii/S2666920X2200073X},
author = {Thomas K.F. Chiu and Qi Xia and Xinyan Zhou and Ching Sing Chai and Miaoting Cheng},
keywords = {Artificial intelligence, Artificial intelligence in education, Systematic review, Learning, Teaching, Assessment},
abstract = {Applications of artificial intelligence in education (AIEd) are emerging and are new to researchers and practitioners alike. Reviews of the relevant literature have not examined how AI technologies have been integrated into each of the four key educational domains of learning, teaching, assessment, and administration. The relationships between the technologies and learning outcomes for students and teachers have also been neglected. This systematic review study aims to understand the opportunities and challenges of AIEd by examining the literature from the last 10 years (2012–2021) using matrix coding and content analysis approaches. The results present the current focus of AIEd research by identifying 13 roles of AI technologies in the key educational domains, 7 learning outcomes of AIEd, and 10 major challenges. The review also provides suggestions for future directions of AIEd research.}
}

@article{Robertson2014ARO,
title = {A Review of Real-Time Strategy Game AI},
author = {Glen Robertson and Ian D. Watson},
affiliation = {University of Auckland },
keywords = {Game, IA, Real-time strategy},
type={article},
language={English},
abstract = {This literature review covers AI techniques used for real-time strategy video games, focusing specifically on StarCraft. It finds that the main areas of current academic research are in tactical and strategic decision making, plan recognition, and learning, and it outlines the research contributions in each of these areas. The paper then contrasts the use of game AI in academe and industry, finding the academic research heavily focused on creating game-winning agents, while the industry aims to maximize player enjoyment. It finds that industry adoption of academic research is low because it is either inapplicable or too time-consuming and risky to implement in a new game, which highlights an area for potential investigation: bridging the gap between academe and industry. Finally, the areas of spatial reasoning, multiscale AI, and cooperation are found to require future work, and standardized evaluation methods are proposed to produce comparable results between studies.},
journal = {AI Mag.},
year = {2014},
volume = {35},
pages = {75-104}
}

@Inproceedings{10.1007/978-3-642-15973-2_50,
author={Butdee, S.
and Tichkiewitch, S.},
affiliation={University of Technology North Bangkok; Grenoble Institute of Technology},
editor={Bernard, Alain},
title={Case-Based Reasoning for Adaptive Aluminum Extrusion Die Design Together with Parameters by Neural Networks},
keywords={Adaptive die design and parameters, Optimal aluminum extrusion, Case-based reasoning, Neural networks},
booktitle={Global Product Development},
year={2011},
type = {article; proceedings paper},
language = {English},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
pages = {491--496},
abstract = {Nowadays Aluminum extrusion die design is a critical task for improving productivity which involves with quality, time and cost. Case-Based Reasoning (CBR) method has been successfully applied to support the die design process in order to design a new die by tackling previous problems together with their solutions to match with a new similar problem. Such solutions are selected and modified to solve the present problem. However, the applications of the CBR are useful only retrieving previous features whereas the critical parameters are missing. In additions, the experience learning to such parameters are limited. This chapter proposes Artificial Neural Network (ANN) to associate the CBR in order to learning previous parameters and predict to the new die design according to the primitive die modification. The most satisfactory is to accommodate the optimal parameters of extrusion processes.},
isbn = {978-3-642-15973-2}
}

@Inproceedings{10.1007/978-3-319-47096-2_11,
author={Grace, Kazjon
and Maher, Mary Lou
and Wilson, David C.
and Najjar, Nadia A.},
affiliation={University of North Carolina at Charlotte},
editor={Goel, Ashok
and D{\'i}az-Agudo, M Bel{\'e}n
and Roth-Berghofer, Thomas},
title={Combining CBR and Deep Learning to Generate Surprising Recipe Designs},
keywords={Case-based reasoning, deep learning, recipe design},
type = {article; proceedings paper},
booktitle={Case-Based Reasoning Research and Development},
year={2016},
publisher={Springer International Publishing},
address={Cham},
language = {English},
pages={154--169},
abstract={This paper presents a dual-cycle CBR model in the domain of recipe generation. The model combines the strengths of deep learning and similarity-based retrieval to generate recipes that are novel and valuable (i.e. they are creative). The first cycle generates abstract descriptions which we call ``design concepts'' by synthesizing expectations from the entire case base, while the second cycle uses those concepts to retrieve and adapt objects. We define these conceptual object representations as an abstraction over complete cases on which expectations can be formed, allowing objects to be evaluated for surprisingness (the peak level of unexpectedness in the object, given the case base) and plausibility (the overall similarity of the object to those in the case base). The paper presents a prototype implementation of the model, and demonstrates its ability to generate objects that are simultaneously plausible and surprising, in addition to fitting a user query. This prototype is then compared to a traditional single-cycle CBR system.},
isbn={978-3-319-47096-2}
}

@Inproceedings{10.1007/978-3-319-61030-6_1,
author={Maher, Mary Lou
and Grace, Kazjon},
editor={Aha, David W.
and Lieber, Jean},
affiliation={University of North Carolina at Charlotte},
title={Encouraging Curiosity in Case-Based Reasoning and Recommender Systems},
keywords={Curiosity, Case-based reasoning, Recommender systems},
booktitle={Case-Based Reasoning Research and Development},
year={2017},
publisher={Springer International Publishing},
address={Cham},
pages={3--15},
language = {English},
type = {article; proceedings paper},
abstract={A key benefit of case-based reasoning (CBR) and recommender systems is the use of past experience to guide the synthesis or selection of the best solution for a specific context or user. Typically, the solution presented to the user is based on a value system that privileges the closest match in a query and the solution that performs best when evaluated according to predefined requirements. In domains in which creativity is desirable or the user is engaged in a learning activity, there is a benefit to moving beyond the expected or ``best match'' and include results based on computational models of novelty and surprise. In this paper, models of novelty and surprise are integrated with both CBR and Recommender Systems to encourage user curiosity.},
isbn={978-3-319-61030-6}
}

@Inproceedings{Muller,
author = {Müller, G. and Bergmann, R.},
affiliation={University of Trier},
year = {2015},
month = {01},
language = {English},
type = {article; proceedings paper},
abstract = {This paper presents CookingCAKE,a framework for the adaptation of cooking recipes represented as workflows. CookingCAKE integrates and combines several workflow adaptation approaches applied in process-oriented case based reasoning (POCBR) in a single adaptation framework, thus providing a capable tool for the adaptation of cooking recipes. The available case base of cooking workflows is analyzed to generate adaptation knowledge which is used to adapt a recipe regarding restrictions and resources, which the user may define for the preparation of a dish.},
booktitle = {International Conference on Case-Based Reasoning},
title = {CookingCAKE: A Framework for the adaptation of cooking recipes represented as workflows},
keywords={recipe adaptation, workflow adaptation, workflows, process-oriented, case based reasoning}
}

@Inproceedings{10.1007/978-3-319-24586-7_20,
author={Onta{\~{n}}{\'o}n, S.
and Plaza, E.
and Zhu, J.},
editor={H{\"u}llermeier, Eyke
and Minor, Mirjam},
affiliation={Drexel University; Artificial Intelligence Research Institute CSIC},
title={Argument-Based Case Revision in CBR for Story Generation},
keywords={CBR, Case-based reasoning, Story generation},
booktitle={Case-Based Reasoning Research and Development},
year={2015},
publisher={Springer International Publishing},
address={Cham},
language = {English},
pages={290--305},
type = {article; proceedings paper},
abstract={This paper presents a new approach to case revision in case-based reasoning based on the idea of argumentation. Previous work on case reuse has proposed the use of operations such as case amalgamation (or merging), which generate solutions by combining information coming from different cases. Such approaches are often based on exploring the search space of possible combinations looking for a solution that maximizes a certain criteria. We show how Revise can be performed by arguments attacking specific parts of a case produced by Reuse, and how they can guide and prevent repeating pitfalls in future cases. The proposed approach is evaluated in the task of automatic story generation.},
isbn={978-3-319-24586-7}
}

@Inproceedings{10.1007/978-3-030-58342-2_20,
author={Lepage, Yves
and Lieber, Jean
and Mornard, Isabelle
and Nauer, Emmanuel
and Romary, Julien
and Sies, Reynault},
editor={Watson, Ian
and Weber, Rosina},
title={The French Correction: When Retrieval Is Harder to Specify than Adaptation},
affiliation={Waseda University; Université de Lorraine},
keywords={case-based reasoning, retrieval, analogy, sentence correction},
booktitle={Case-Based Reasoning Research and Development},
year={2020},
language = {English},
type = {article; proceedings paper},
publisher={Springer International Publishing},
address={Cham},
pages={309--324},
abstract={A common idea in the field of case-based reasoning is that the retrieval step can be specified by the use of some similarity measure: the retrieved cases maximize the similarity to the target problem and, then, the adaptation step has to take into account the mismatches between the retrieved cases and the target problem in order to this latter. The use of this methodological schema for the application described in this paper has proven to be non efficient. Indeed, designing a retrieval procedure without the precise knowledge of the adaptation procedure has not been possible. The domain of this application is the correction of French sentences: a problem is an incorrect sentence and a valid solution is a correction of this problem. Adaptation consists in solving an analogical equation that enables to execute the correction of the retrieved case on the target problem. Thus, retrieval has to ensure that this application is feasible. The first version of such a retrieval procedure is described and evaluated: it is a knowledge-light procedure that does not use linguistic knowledge about French.},
isbn={978-3-030-58342-2}
}

@Inproceedings{10.1007/978-3-030-01081-2_25,
author={Smyth, Barry
and Cunningham, P{\'a}draig},
editor={Cox, Michael T.
and Funk, Peter
and Begum, Shahina},
affiliation={University College Dublin},
title={An Analysis of Case Representations for Marathon Race Prediction and Planning},
keywords={Marathon planning, Case representation, Case-based reasoning},
booktitle={Case-Based Reasoning Research and Development},
year={2018},
language = {English},
publisher={Springer International Publishing},
address={Cham},
pages={369--384},
type = {article; proceedings paper},
abstract={We use case-based reasoning to help marathoners achieve a personal best for an upcoming race, by helping them to select an achievable goal-time and a suitable pacing plan. We evaluate several case representations and, using real-world race data, highlight their performance implications. Richer representations do not always deliver better prediction performance, but certain representational configurations do offer very significant practical benefits for runners, when it comes to predicting, and planning for, challenging goal-times during an upcoming race.},
isbn={978-3-030-01081-2}
}

@Inproceedings{10.1007/978-3-030-58342-2_8,
author={Smyth, Barry
and Willemsen, Martijn C.},
editor={Watson, Ian
and Weber, Rosina},
affiliation={University College Dublin; Eindhoven University of Technology},
title={Predicting the Personal-Best Times of Speed Skaters Using Case-Based Reasoning},
keywords={CBR for health and exercise, speed skating, race-time prediction, case representation},
booktitle={Case-Based Reasoning Research and Development},
year={2020},
type = {article; proceedings paper},
language = {English},
publisher={Springer International Publishing},
address={Cham},
pages={112--126},
abstract={Speed skating is a form of ice skating in which the skaters race each other over a variety of standardised distances. Races take place on specialised ice-rinks and the type of track and ice conditions can have a significant impact on race-times. As race distances increase, pacing also plays an important role. In this paper we seek to extend recent work on the application of case-based reasoning to marathon-time prediction by predicting race-times for speed skaters. In particular, we propose and evaluate a number of case-based reasoning variants based on different case and feature representations to generate track-specific race predictions. We show it is possible to improve upon state-of-the-art prediction accuracy by harnessing richer case representations using shorter races and track-adjusted finish and lap-times.},
isbn={978-3-030-58342-2}
}

@Inproceedings{10.1007/978-3-030-58342-2_5,
author={Feely, Ciara
and Caulfield, Brian
and Lawlor, Aonghus
and Smyth, Barry},
editor={Watson, Ian
and Weber, Rosina},
affiliation={University College Dublin},
title={Using Case-Based Reasoning to Predict Marathon Performance and Recommend Tailored Training Plans},
keywords={CBR for health and exercise, marathon running, race-time prediction, plan recommendation},
booktitle={Case-Based Reasoning Research and Development},
year={2020},
language = {English},
publisher={Springer International Publishing},
address={Cham},
pages={67--81},
type = {article; proceedings paper},
abstract={Training for the marathon, especially a first marathon, is always a challenge. Many runners struggle to find the right balance between their workouts and their recovery, often leading to sub-optimal performance on race-day or even injury during training. We describe and evaluate a novel case-based reasoning system to help marathon runners as they train in two ways. First, it uses a case-base of training/workouts and race histories to predict future marathon times for a target runner, throughout their training program, helping runners to calibrate their progress and, ultimately, plan their race-day pacing. Second, the system recommends tailored training plans to runners, adapted for their current goal-time target, and based on the training plans of similar runners who have achieved this time. We evaluate the system using a dataset of more than 21,000 unique runners and 1.5 million training/workout sessions.},
isbn={978-3-030-58342-2}
}

@article{LALITHA2020583,
title = {Personalised Self-Directed Learning Recommendation System},
journal = {Procedia Computer Science},
volume = {171},
pages = {583-592},
year = {2020},
type = {article},
language = {English},
note = {Third International Conference on Computing and Network Communications (CoCoNet'19)},
issn = {1877-0509},
doi = {https://doi.org/10.1016/j.procs.2020.04.063},
url = {https://www.sciencedirect.com/science/article/pii/S1877050920310309},
author = {T B Lalitha and P S Sreeja},
affiliation={Hindustan Institute of Technology and Science},
keywords = {e-Learning, PSDLR, Recommendation System, SDL, Self-Directed Learning},
abstract = {Modern educational systems have changed drastically bringing in knowledge anywhere as needed by the learner with the evolution of Internet. Availability of knowledge in public domain, capability of exchanging large amount of information and filtering relevant information quickly has enabled disruption to conventional educational system. Thus, future trends are looking towards E-Learning (Electronic Learning) and M-Learning (Mobile Learning) technologies over the Internet for their vast knowledge acquisition. In this paper, the work gives an elaborate context of learning strategies prevailing and emerging with the classification of e-learning Techniques. It majorly focuses on the features and variety of aspects with the e-learning and the choice of learning method involved and facilitate the adoption of new ways for personalized selection on learning resources for SDL (Self-Directed Learning) from the unstructured, large web-based environment. Thereby, proposes a Personalised Self-Directed Learning Recommendation System (PSDLR) based on the personal specifications of the SDL learner. The result offers insight into the perspectives and challenges of Self-Directed Learning based on cognitive and constructive characteristics which majorly incorporates web-based learning and gives path in finding appropriate solutions using machine learning techniques and ontology for the open problems in the respective fields with personalised recommendations and guidelines for future research.}
}

@article{Zhou2021,
author={Zhou, Lina
and Wang, Chunxia},
affiliation={Baotou Medical College},
title={Research on Recommendation of Personalized Exercises in English Learning Based on Data Mining},
journal={Scientific Programming},
year={2021},
month={Dec},
type = {article},
language = {English},
day={21},
publisher={Hindawi},
keywords={Recommender systems, Learning},
volume={2021},
pages={5042286},
abstract={Aiming at the problems of traditional method of exercise recommendation precision, recall rate, long recommendation time, and poor recommendation comprehensiveness, this study proposes a personalized exercise recommendation method for English learning based on data mining. Firstly, a personalized recommendation model is designed, based on the model to preprocess the data in the Web access log, and cleaning the noise data to avoid its impact on the accuracy of the recommendation results is focused; secondly, the DINA model to diagnose the degree of mastery of students{\&}{\#}x2019; knowledge points is used and the students{\&}{\#}x2019; browsing patterns through fuzzy similar relationships are clustered; and finally, according to the clustering results, the similarity between students and the similarity between exercises are measured, and the collaborative filtering recommendation of personalized exercises for English learning is realized. The experimental results show that the exercise recommendation precision and recall rate of this method are higher, the recommendation time is shorter, and the recommendation results are comprehensive.},
issn={1058-9244},
doi={10.1155/2021/5042286},
url={https://doi.org/10.1155/2021/5042286}
}

@article{INGKAVARA2022100086,
title = {The use of a personalized learning approach to implementing self-regulated online learning},
journal = {Computers and Education: Artificial Intelligence},
volume = {3},
pages = {100086},
type = {article},
language = {English},
year = {2022},
issn = {2666-920X},
doi = {https://doi.org/10.1016/j.caeai.2022.100086},
url = {https://www.sciencedirect.com/science/article/pii/S2666920X22000418},
author = {Thanyaluck Ingkavara and Patcharin Panjaburee and Niwat Srisawasdi and Suthiporn Sajjapanroj},
keywords = {Intelligent tutoring system, Personalization, Adaptive learning, E-learning, TAM, Artificial intelligence},
abstract = {Nowadays, students are encouraged to learn via online learning systems to promote students' autonomy. Scholars have found that students' self-regulated actions impact their academic success in an online learning environment. However, because traditional online learning systems cannot personalize feedback to the student's personality, most students have less chance to obtain helpful suggestions for enhancing their knowledge linked to their learning problems. This paper incorporated self-regulated online learning in the Physics classroom and used a personalized learning approach to help students receive proper learning paths and material corresponding to their learning preferences. This study conducted a quasi-experimental design using a quantitative approach to evaluate the effectiveness of the proposed learning environment in secondary schools. The experimental group of students participated in self-regulated online learning with a personalized learning approach, while the control group participated in conventional self-regulated online learning. The experimental results showed that the experimental group's post-test and the learning-gain score of the experimental group were significantly higher than those of the control group. Moreover, the results also suggested that the student's perceptions about the usefulness of learning suggestions, ease of use, goal setting, learning environmental structuring, task strategies, time management, self-evaluation, impact on learning, and attitude toward the learning environment are important predictors of behavioral intention to learn with the self-regulated online learning that integrated with the personalized learning approach.}
}

@article{HUANG2023104684,
title = {Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom},
journal = {Computers and Education},
volume = {194},
pages = {104684},
year = {2023},
language = {English},
type = {article},
issn = {0360-1315},
doi = {https://doi.org/10.1016/j.compedu.2022.104684},
url = {https://www.sciencedirect.com/science/article/pii/S036013152200255X},
author = {Anna Y.Q. Huang and Owen H.T. Lu and Stephen J.H. Yang},
keywords = {Data science applications in education, Distance education and online learning, Improving classroom teaching},
abstract = {The flipped classroom approach is aimed at improving learning outcomes by promoting learning motivation and engagement. Recommendation systems can also be used to improve learning outcomes. With the rapid development of artificial intelligence (AI) technology, various systems have been developed to facilitate student learning. Accordingly, we applied AI-enabled personalized video recommendations to stimulate students' learning motivation and engagement during a systems programming course in a flipped classroom setting. We assigned students to control and experimental groups comprising 59 and 43 college students, respectively. The students in both groups received flipped classroom instruction, but only those in the experimental group received AI-enabled personalized video recommendations. We quantitatively measured students’ engagement based on their learning profiles in a learning management system. The results revealed that the AI-enabled personalized video recommendations could significantly improve the learning performance and engagement of students with a moderate motivation level.}
}

@article{ZHAO2023118535,
title = {A recommendation system for effective learning strategies: An integrated approach using context-dependent DEA},
journal = {Expert Systems with Applications},
volume = {211},
pages = {118535},
year = {2023},
language = {English},
type = {article},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2022.118535},
url = {https://www.sciencedirect.com/science/article/pii/S0957417422016104},
author = {Lu-Tao Zhao and Dai-Song Wang and Feng-Yun Liang and Jian Chen},
keywords = {Recommendation system, Learning strategies, Context-dependent DEA, Efficiency analysis},
abstract = {Universities have been focusing on increasing individualized training and providing appropriate education for students. The individual differences and learning needs of college students should be given enough attention. From the perspective of learning efficiency, we establish a clustering hierarchical progressive improvement model (CHPI), which is based on cluster analysis and context-dependent data envelopment analysis (DEA) methods. The CHPI clusters students' ontological features, employs the context-dependent DEA method to stratify students of different classes, and calculates measures, such as obstacles, to determine the reference path for individuals with inefficient learning processes. The learning strategies are determined according to the gap between the inefficient individual to be improved and the individuals on the reference path. By the study of college English courses as an example, it is found that the CHPI can accurately recommend targeted learning strategies to satisfy the individual needs of college students so that the learning of individuals with inefficient learning processes in a certain stage can be effectively improved. In addition, CHPI can provide specific, efficient suggestions to improve learning efficiency comparing to existing recommendation systems, and has great potential in promoting the integration of education-related researches and expert systems.}
}

@article{SU2022109547,
title = {Graph-based cognitive diagnosis for intelligent tutoring systems},
journal = {Knowledge-Based Systems},
volume = {253},
pages = {109547},
year = {2022},
language = {English},
type = {article},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2022.109547},
url = {https://www.sciencedirect.com/science/article/pii/S095070512200778X},
author = {Yu Su and Zeyu Cheng and Jinze Wu and Yanmin Dong and Zhenya Huang and Le Wu and Enhong Chen and Shijin Wang and Fei Xie},
keywords = {Cognitive diagnosis, Graph neural networks, Interpretable machine learning},
abstract = {For intelligent tutoring systems, Cognitive Diagnosis (CD) is a fundamental task that aims to estimate the mastery degree of a student on each skill according to the exercise record. The CD task is considered rather challenging since we need to model inner-relations and inter-relations among students, skills, and questions to obtain more abundant information. Most existing methods attempt to solve this problem through two-way interactions between students and questions (or between students and skills), ignoring potential high-order relations among entities. Furthermore, how to construct an end-to-end framework that can model the complex interactions among different types of entities at the same time remains unexplored. Therefore, in this paper, we propose a graph-based Cognitive Diagnosis model (GCDM) that directly discovers the interactions among students, skills, and questions through a heterogeneous cognitive graph. Specifically, we design two graph-based layers: a performance-relative propagator and an attentive knowledge aggregator. The former is applied to propagate a student’s cognitive state through different types of graph edges, while the latter selectively gathers messages from neighboring graph nodes. Extensive experimental results on two real-world datasets clearly show the effectiveness and extendibility of our proposed model.}
}

@article{EZALDEEN2022100700,
title = {A hybrid E-learning recommendation integrating adaptive profiling and sentiment analysis},
journal = {Journal of Web Semantics},
volume = {72},
pages = {100700},
year = {2022},
type = {article},
language = {English},
issn = {1570-8268},
doi = {https://doi.org/10.1016/j.websem.2021.100700},
url = {https://www.sciencedirect.com/science/article/pii/S1570826821000664},
author = {Hadi Ezaldeen and Rachita Misra and Sukant Kishoro Bisoy and Rawaa Alatrash and Rojalina Priyadarshini},
keywords = {Hybrid E-learning recommendation, Adaptive profiling, Semantic learner profile, Fine-grained sentiment analysis, Convolutional Neural Network, Word embeddings},
abstract = {This research proposes a novel framework named Enhanced e-Learning Hybrid Recommender System (ELHRS) that provides an appropriate e-content with the highest predicted ratings corresponding to the learner’s particular needs. To accomplish this, a new model is developed to deduce the Semantic Learner Profile automatically. It adaptively associates the learning patterns and rules depending on the learner’s behavior and the semantic relations computed in the semantic matrix that mutually links e-learning materials and terms. Here, a semantic-based approach for term expansion is introduced using DBpedia and WordNet ontologies. Further, various sentiment analysis models are proposed and incorporated as a part of the recommender system to predict ratings of e-learning resources from posted text reviews utilizing fine-grained sentiment classification on five discrete classes. Qualitative Natural Language Processing (NLP) methods with tailored-made Convolutional Neural Network (CNN) are developed and evaluated on our customized dataset collected for a specific domain and a public dataset. Two improved language models are introduced depending on Skip-Gram (S-G) and Continuous Bag of Words (CBOW) techniques. In addition, a robust language model based on hybridization of these couple of methods is developed to derive better vocabulary representation, yielding better accuracy 89.1% for the CNN-Three-Channel-Concatenation model. The suggested recommendation methodology depends on the learner’s preferences, other similar learners’ experience and background, deriving their opinions from the reviews towards the best learning resources. This assists the learners in finding the desired e-content at the proper time.}
}

@article{MUANGPRATHUB2020e05227,
title = {Learning recommendation with formal concept analysis for intelligent tutoring system},
journal = {Heliyon},
volume = {6},
number = {10},
pages = {e05227},
language = {English},
type = {article},
year = {2020},
issn = {2405-8440},
doi = {https://doi.org/10.1016/j.heliyon.2020.e05227},
url = {https://www.sciencedirect.com/science/article/pii/S2405844020320703},
author = {Jirapond Muangprathub and Veera Boonjing and Kosin Chamnongthai},
keywords = {Computer Science, Learning recommendation, Formal concept analysis, Intelligent tutoring system, Adaptive learning},
abstract = {The aim of this research was to develop a learning recommendation component in an intelligent tutoring system (ITS) that dynamically predicts and adapts to a learner's style. In order to develop a proper ITS, we present an improved knowledge base supporting adaptive learning, which can be achieved by a suitable knowledge construction. This process is illustrated by implementing a web-based online tutor system. In addition, our knowledge structure provides adaptive presentation and personalized learning with the proposed adaptive algorithm, to retrieve content according to individual learner characteristics. To demonstrate the proposed adaptive algorithm, pre-test and post-test were used to evaluate suggestion accuracy of the course in a class for adapting to a learner's style. In addition, pre- and post-testing were also used with students in a real teaching/learning environment to evaluate the performance of the proposed model. The results show that the proposed system can be used to help students or learners achieve improved learning.}
}

@article{min8100434,
author = {Leikola, Maria and Sauer, Christian and Rintala, Lotta and Aromaa, Jari and Lundström, Mari},
title = {Assessing the Similarity of Cyanide-Free Gold Leaching Processes: A Case-Based Reasoning Application},
journal = {Minerals},
volume = {8},
type = {article},
language = {English},
year = {2018},
number = {10},
url = {https://www.mdpi.com/2075-163X/8/10/434},
issn = {2075-163X},
keywords={hydrometallurgy, cyanide-free gold, knowledge modelling, case-based reasoning, information retrieval},
abstract = {Hydrometallurgical researchers, and other professionals alike, invest significant amounts of time reading scientific articles, technical notes, and other scientific documents, while looking for the most relevant information for their particular research interest. In an attempt to save the researcher&rsquo;s time, this study presents an information retrieval tool using case-based reasoning. The tool was built for comparing scientific articles concerning cyanide-free leaching of gold ores/concentrates/tailings. Altogether, 50 cases of experiments were gathered in a case base. 15 different attributes related to the treatment of the raw material and the leaching conditions were selected to compare the cases. The attributes were as follows: Pretreatment, Overall method, Complexant source, Oxidant source, Complexant concentration, Oxidant concentration, Temperature, pH, Redox-potential, Pressure, Materials of construction, Extraction, Extraction rate, Reagent consumption, and Solid-liquid ratio. The resulting retrieval tool (LeachSim) was able to rank the scientific articles according to their similarity with the user&rsquo;s research interest. Such a tool could eventually aid the user in finding the most relevant information, but not replace thorough understanding and human expertise.},
doi = {10.3390/min8100434}
}

@article{10.1145/3459665,
author = {Cunningham, P\'{a}draig and Delany, Sarah Jane},
title = {K-Nearest Neighbour Classifiers - A Tutorial},
year = {2021},
issue_date = {July 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
type={article},
language={English},
volume = {54},
number = {6},
issn = {0360-0300},
url = {https://doi.org/10.1145/3459665},
doi = {10.1145/3459665},
abstract = {Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nearest Neighbour Classifier—classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance, because issues of poor runtime performance is not such a problem these days with the computational power that is available. This article presents an overview of techniques for Nearest Neighbour classification focusing on: mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours, and mechanisms for reducing the dimension of the data.This article is the second edition of a paper previously published as a technical report [16]. Sections on similarity measures for time-series, retrieval speedup, and intrinsic dimensionality have been added. An Appendix is included, providing access to Python code for the key methods.},
journal = {ACM Comput. Surv.},
month = {jul},
articleno = {128},
numpages = {25},
keywords = {k-Nearest neighbour classifiers}
}

@article{9072123,
author={Sinaga, Kristina P. and Yang, Miin-Shen},
journal={IEEE Access}, 
type={article},
language={English},
title={Unsupervised K-Means Clustering Algorithm}, 
year={2020},
volume={8},
number={},
pages={80716-80727},
doi={10.1109/ACCESS.2020.2988796}
}

@article{WANG2021331,
title = {A new prediction strategy for dynamic multi-objective optimization using Gaussian Mixture Model},
journal = {Information Sciences},
volume = {580},
type = {article},
language = {English},
pages = {331-351},
year = {2021},
issn = {0020-0255},
doi = {https://doi.org/10.1016/j.ins.2021.08.065},
url = {https://www.sciencedirect.com/science/article/pii/S0020025521008732},
author = {Feng Wang and Fanshu Liao and Yixuan Li and Hui Wang},
keywords = {Dynamic multi-objective optimization, Gaussian Mixture Model, Change type detection, Resampling},
abstract = {Dynamic multi-objective optimization problems (DMOPs), in which the environments change over time, have attracted many researchers’ attention in recent years. Since the Pareto set (PS) or the Pareto front (PF) can change over time, how to track the movement of the PS or PF is a challenging problem in DMOPs. Over the past few years, lots of methods have been proposed, and the prediction based strategy has been considered the most effective way to track the new PS. However, the performance of most existing prediction strategies depends greatly on the quantity and quality of the historical information and will deteriorate due to non-linear changes, leading to poor results. In this paper, we propose a new prediction method, named MOEA/D-GMM, which incorporates the Gaussian Mixture Model (GMM) into the MOEA/D framework for the prediction of the new PS when changes occur. Since GMM is a powerful non-linear model to accurately fit various data distributions, it can effectively generate solutions with better quality according to the distributions. In the proposed algorithm, a change type detection strategy is first designed to estimate an approximate PS according to different change types. Then, GMM is employed to make a more accurate prediction by training it with the approximate PS. To overcome the shortcoming of a lack of training solutions for GMM, the Empirical Cumulative Distribution Function (ECDF) method is used to resample more training solutions before GMM training. Experimental results on various benchmark test problems and a classical real-world problem show that, compared with some state-of-the-art dynamic optimization algorithms, MOEA/D-GMM outperforms others in most cases.}
}

@article{9627973,
author={Xu, Shengbing and Cai, Wei and Xia, Hongxi and Liu, Bo and Xu, Jie},
journal={IEEE Access}, 
title={Dynamic Metric Accelerated Method for Fuzzy Clustering}, 
year={2021},
type={article},
language={English},
volume={9},
number={},
pages={166838-166854},
doi={10.1109/ACCESS.2021.3131368}
}

@article{9434422,
author={Gupta, Samarth and Chaudhari, Shreyas and Joshi, Gauri and Yağan, Osman},
journal={IEEE Transactions on Information Theory}, 
title={Multi-Armed Bandits With Correlated Arms}, 
year={2021},
language={English},
type={article},
volume={67},
number={10},
pages={6711-6732},
doi={10.1109/TIT.2021.3081508}
}

@Inproceedings{8495930,
author={Supic, H.},
booktitle={2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)}, 
title={Case-Based Reasoning Model for Personalized Learning Path Recommendation in Example-Based Learning Activities}, 
year={2018},
type={article},
language={English},
volume={},
number={},
pages={175-178},
doi={10.1109/WETICE.2018.00040}
}

@Inproceedings{9870279,
author={Lin, Baihan},
booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)}, 
title={Evolutionary Multi-Armed Bandits with Genetic Thompson Sampling}, 
year={2022},
type={article},
language={English},
volume={},
number={},
pages={1-8},
doi={10.1109/CEC55065.2022.9870279}
}

@article{Obeid,
author={Obeid, C. and Lahoud, C. and Khoury, H. E. and Champin, P.},
title={A Novel Hybrid Recommender System Approach for Student Academic Advising Named COHRS, Supported by Case-based Reasoning and Ontology},
journal={Computer Science and Information Systems},
type={article},
language={English},
volume={19},
number={2},
pages={979–1005},
year={2022},
doi={https://doi.org/10.2298/CSIS220215011O}
}

@book{Nkambou,
author = {Nkambou, R. and Bourdeau, J. and Mizoguchi, R.},
title = {Advances in Intelligent Tutoring Systems},
year = {2010},
type = {article},
language = {English},
publisher = {Springer Berlin, Heidelberg},
edition = {1}
}

@book{hajduk2019cognitive,
title={Cognitive Multi-agent Systems: Structures, Strategies and Applications to Mobile Robotics and Robosoccer},
author={Hajduk, M. and Sukop, M. and Haun, M.},
type={book},
language={English},
isbn={9783319936857},
series={Studies in Systems, Decision and Control},
year={2019},
publisher={Springer International Publishing}
}

@article{RICHTER20093,
title = {The search for knowledge, contexts, and Case-Based Reasoning},
journal = {Engineering Applications of Artificial Intelligence},
language = {English},
type = {article},
volume = {22},
number = {1},
pages = {3-9},
year = {2009},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2008.04.021},
url = {https://www.sciencedirect.com/science/article/pii/S095219760800078X},
author = {Michael M. Richter},
keywords = {Case-Based Reasoning, Knowledge, Processes, Utility, Context},
abstract = {A major goal of this paper is to compare Case-Based Reasoning with other methods searching for knowledge. We consider knowledge as a resource that can be traded. It has no value in itself; the value is measured by the usefulness of applying it in some process. Such a process has info-needs that have to be satisfied. The concept to measure this is the economical term utility. In general, utility depends on the user and its context, i.e., it is subjective. Here, we introduce levels of contexts from general to individual. We illustrate that Case-Based Reasoning on the lower, i.e., more personal levels CBR is quite useful, in particular in comparison with traditional informational retrieval methods.}
}

@Thesis{Marie,
author={Marie, F.},
title={COLISEUM-3D. Une plate-forme innovante pour la segmentation d’images médicales par Raisonnement à Partir de Cas (RàPC) et méthodes d’apprentissage de type Deep Learning},
type={diplomathesis},
language={French},
institution={Université de Franche-Comte},
year={2019}
}

@book{Hoang,
title = {La formule du savoir. Une philosophie unifiée du savoir fondée sur le théorème de Bayes},
author = {Hoang, L.N.},
type = {book},
language = {French},
isbn = {9782759822607},
year = {2018},
publisher = {EDP Sciences}
}

@book{Richter2013,
title={Case-Based Reasoning (A Textbook)},
author={Richter, M. and Weber, R.},
type={book},
language={English},
isbn={9783642401664},
year={2013},
publisher={Springer-Verlag GmbH}
}

@book{kedia2020hands,
title={Hands-On Python Natural Language Processing: Explore tools and techniques to analyze and process text with a view to building real-world NLP applications},
author={Kedia, A. and Rasu, M.},
language={English},
type={book},
isbn={9781838982584},
url={https://books.google.fr/books?id=1AbuDwAAQBAJ},
year={2020},
publisher={Packt Publishing}
}

@book{ghosh2019natural,
title={Natural Language Processing Fundamentals: Build intelligent applications that can interpret the human language to deliver impactful results},
author={Ghosh, S. and Gunning, D.},
language={English},
type={book},
isbn={9781789955989},
url={https://books.google.fr/books?id=i8-PDwAAQBAJ},
year={2019},
publisher={Packt Publishing}
}

@article{Akerblom,
title={Online learning of network bottlenecks via minimax paths},
author={kerblom, Niklas and Hoseini, Fazeleh Sadat and Haghir Chehreghani, Morteza},
language={English},
type={article},
volume = {122},
year = {2023},
issn = {1573-0565},
doi = {https://doi.org/10.1007/s10994-022-06270-0},
url = {https://doi.org/10.1007/s10994-022-06270-0},
abstract={In this paper, we study bottleneck identification in networks via extracting minimax paths. Many real-world networks have stochastic weights for which full knowledge is not available in advance. Therefore, we model this task as a combinatorial semi-bandit problem to which we apply a combinatorial version of Thompson Sampling and establish an upper bound on the corresponding Bayesian regret. Due to the computational intractability of the problem, we then devise an alternative problem formulation which approximates the original objective. Finally, we experimentally evaluate the performance of Thompson Sampling with the approximate formulation on real-world directed and undirected networks.}
}

@article{Simen,
title={Dynamic slate recommendation with gated recurrent units and Thompson sampling},
author={Eide, Simen and Leslie, David S. and Frigessi, Arnoldo},
language={English},
type={article},
volume = {36},
year = {2022},
issn = {1573-756X},
doi = {https://doi.org/10.1007/s10618-022-00849-w},
url = {https://doi.org/10.1007/s10618-022-00849-w},
abstract={We consider the problem of recommending relevant content to users of an internet platform in the form of lists of items, called slates. We introduce a variational Bayesian Recurrent Neural Net recommender system that acts on time series of interactions between the internet platform and the user, and which scales to real world industrial situations. The recommender system is tested both online on real users, and on an offline dataset collected from a Norwegian web-based marketplace, FINN.no, that is made public for research. This is one of the first publicly available datasets which includes all the slates that are presented to users as well as which items (if any) in the slates were clicked on. Such a data set allows us to move beyond the common assumption that implicitly assumes that users are considering all possible items at each interaction. Instead we build our likelihood using the items that are actually in the slate, and evaluate the strengths and weaknesses of both approaches theoretically and in experiments. We also introduce a hierarchical prior for the item parameters based on group memberships. Both item parameters and user preferences are learned probabilistically. Furthermore, we combine our model with bandit strategies to ensure learning, and introduce ‘in-slate Thompson sampling’ which makes use of the slates to maximise explorative opportunities. We show experimentally that explorative recommender strategies perform on par or above their greedy counterparts. Even without making use of exploration to learn more effectively, click rates increase simply because of improved diversity in the recommended slates.}
}

@Inproceedings{Arthurs,
author={Arthurs, Noah and Stenhaug, Ben and Karayev, Sergey and Piech, Chris},
booktitle={International Conference on Educational Data Mining (EDM)},
title={Grades Are Not Normal: Improving Exam Score Models Using the Logit-Normal Distribution}, 
year={2019},
type={article},
language={English},
volume={},
number={},
pages={6},
url={https://eric.ed.gov/?id=ED599204}
}

@article{Bahramian,
title={A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning},
author={Bahramian, Zahra and Ali Abbaspour, Rahim and Claramunt, Christophe},
language={English},
type={article},
year = {2017},
issn = {1574-017X},
doi = {https://doi.org/10.1155/2017/9364903},
url = {https://doi.org/10.1155/2017/9364903},
abstract={Nowadays, large amounts of tourism information and services are available over the Web. This makes it difficult for the user to search for some specific information such as selecting a tour in a given city as an ordered set of points of interest. Moreover, the user rarely knows all his needs upfront and his preferences may change during a recommendation process. The user may also have a limited number of initial ratings and most often the recommender system is likely to face the well-known cold start problem. The objective of the research presented in this paper is to introduce a hybrid interactive context-aware tourism recommender system that takes into account user’s feedbacks and additional contextual information. It offers personalized tours to the user based on his preferences thanks to the combination of a case based reasoning framework and an artificial neural network. The proposed method has been tried in the city of Tehran in Iran. The results show that the proposed method outperforms current artificial neural network methods and combinations of case based reasoning with <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063999pt" id="M1" height="9.49473pt" version="1.1" viewBox="-0.0498162 -9.28833 6.66314 9.49473" width="6.66314pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-108" d="M480 416C480 431 465 448 438 448C388 448 312 383 252 330C217 299 188 273 155 237H153L257 680C262 700 263 712 253 712C240 712 183 684 97 674L92 648L126 647C166 646 172 645 163 606L23 -6L29 -12C51 -5 77 2 107 8C115 62 130 128 142 180C153 193 179 220 204 241C231 170 259 106 288 54C317 0 336 -12 358 -12C381 -12 423 2 477 80L460 100C434 74 408 54 398 54C385 54 374 65 351 107C326 154 282 241 263 299C296 332 351 377 403 377C424 377 436 372 445 368C449 366 456 368 462 375C472 386 480 402 480 416Z"/></g></svg>-nearest neighbor methods in terms of user effort, accuracy, and user satisfaction.}
}

@Thesis{Daubias2011,
author={Sthéphanie Jean-Daubias},
title={Ingénierie des profils d'apprenants},
type={diplomathesis},
language={French},
institution={Université Claude Bernard Lyon 1},
year={2011}
}

@article{Tapalova,
author = {Olga Tapalova and Nadezhda Zhiyenbayeva},
title ={Artificial Intelligence in Education: AIEd for Personalised Learning Pathways},
journal = {Electronic Journal of e-Learning},
volume = {},
number = {},
pages = {15},
year = {2022},
URL = {https://eric.ed.gov/?q=Artificial+Intelligence+in+Education%3a+AIEd+for+Personalised+Learning+Pathways&id=EJ1373006},
language={English},
type={article},
abstract = {Artificial intelligence is the driving force of change focusing on the needs and demands of the student. The research explores Artificial Intelligence in Education (AIEd) for building personalised learning systems for students. The research investigates and proposes a framework for AIEd: social networking sites and chatbots, expert systems for education, intelligent mentors and agents, machine learning, personalised educational systems and virtual educational environments. These technologies help educators to develop and introduce personalised approaches to master new knowledge and develop professional competencies. The research presents a case study of AIEd implementation in education. The scholars conducted the experiment in educational establishments using artificial intelligence in the curriculum. The scholars surveyed 184 second-year students of the Institute of Pedagogy and Psychology at the Abay Kazakh National Pedagogical University and the Kuban State Technological University to collect the data. The scholars considered the collective group discussions regarding the application of artificial intelligence in education to improve the effectiveness of learning. The research identified key advantages to creating personalised learning pathways such as access to training in 24/7 mode, training in virtual contexts, adaptation of educational content to personal needs of students, real-time and regular feedback, improvements in the educational process and mental stimulations. The proposed education paradigm reflects the increasing role of artificial intelligence in socio-economic life, the social and ethical concerns artificial intelligence may pose to humanity and its role in the digitalisation of education. The current article may be used as a theoretical framework for many educational institutions planning to exploit the capabilities of artificial intelligence in their adaptation to personalized learning.}
}

@article{Auer,
title = {From monolithic systems to Microservices: An assessment framework},
journal = {Information and Software Technology},
volume = {137},
pages = {106600},
year = {2021},
issn = {0950-5849},
doi = {https://doi.org/10.1016/j.infsof.2021.106600},
url = {https://www.sciencedirect.com/science/article/pii/S0950584921000793},
author = {Florian Auer and Valentina Lenarduzzi and Michael Felderer and Davide Taibi},
keywords = {Microservices, Cloud migration, Software measurement},
abstract = {Context:
Re-architecting monolithic systems with Microservices-based architecture is a common trend. Various companies are migrating to Microservices for different reasons. However, making such an important decision like re-architecting an entire system must be based on real facts and not only on gut feelings.
Objective:
The goal of this work is to propose an evidence-based decision support framework for companies that need to migrate to Microservices, based on the analysis of a set of characteristics and metrics they should collect before re-architecting their monolithic system.
Method:
We conducted a survey done in the form of interviews with professionals to derive the assessment framework based on Grounded Theory.
Results:
We identified a set consisting of information and metrics that companies can use to decide whether to migrate to Microservices or not. The proposed assessment framework, based on the aforementioned metrics, could be useful for companies if they need to migrate to Microservices and do not want to run the risk of failing to consider some important information.}
}

@Article{jmse10040464,
AUTHOR = {Zuluaga, Carlos A. and Aristizábal, Luis M. and Rúa, Santiago and Franco, Diego A. and Osorio, Dorie A. and Vásquez, Rafael E.},
TITLE = {Development of a Modular Software Architecture for Underwater Vehicles Using Systems Engineering},
JOURNAL = {Journal of Marine Science and Engineering},
VOLUME = {10},
YEAR = {2022},
NUMBER = {4},
ARTICLE-NUMBER = {464},
URL = {https://www.mdpi.com/2077-1312/10/4/464},
ISSN = {2077-1312},
ABSTRACT = {This paper addresses the development of a modular software architecture for the design/construction/operation of a remotely operated vehicle (ROV), based on systems engineering. First, systems engineering and the Vee model are presented with the objective of defining the interactions of the stakeholders with the software architecture development team and establishing the baselines that must be met in each development phase. In the development stage, the definition of the architecture and its connection with the hardware is presented, taking into account the use of the actor model, which represents the high-level software architecture used to solve concurrency problems. Subsequently, the structure of the classes is defined both at high and low levels in the instruments using the object-oriented programming paradigm. Finally, unit tests are developed for each component in the software architecture, quality assessment tests are implemented for system functions fulfillment, and a field sea trial for testing different modules of the vehicle is described. This approach is well suited for the development of complex systems such as marine vehicles and those systems which require scalability and modularity to add functionalities.},
DOI = {10.3390/jmse10040464}
}

@article{doi10.1177/1754337116651013,
author = {Julien Henriet and Lang Christophe and Philippe Laurent},
title ={Artificial Intelligence-Virtual Trainer: An educative system based on artificial intelligence and designed to produce varied and consistent training lessons},
journal = {Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology},
volume = {231},
number = {2},
pages = {110-124},
year = {2017},
doi = {10.1177/1754337116651013},
URL = {https://doi.org/10.1177/1754337116651013},
eprint = {https://doi.org/10.1177/1754337116651013},
abstract = { AI-Virtual Trainer is an educative system using Artificial Intelligence to propose varied lessons to trainers. The agents of this multi-agent system apply case-based reasoning to build solutions by analogy. However, as required by the field, Artificial Intelligence-Virtual Trainer never proposes the same lesson twice, whereas the same objective may be set many times consecutively. The adaptation process of Artificial Intelligence-Virtual Trainer delivers an ordered set of exercises adapted to the objectives and sub-objectives chosen by trainers. This process has been enriched by including the notion of distance between exercises: the proposed tasks are not only appropriate but are hierarchically ordered. With this new version of the system, students are guided towards their objectives via an underlying theme. Finally, the agents responsible for the different parts of lessons collaborate with each other according to a dedicated protocol and decision-making policy since no exercise must appear more than once in the same lesson. The results prove that Artificial Intelligence-Virtual Trainer, however perfectible, meets the requirements of this field. }
}

@InProceedings{10.1007/978-3-030-01081-2_9,
author="Henriet, Julien
and Greffier, Fran{\c{c}}oise",
editor="Cox, Michael T.
and Funk, Peter
and Begum, Shahina",
title="AI-VT: An Example of CBR that Generates a Variety of Solutions to the Same Problem",
booktitle="Case-Based Reasoning Research and Development",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="124--139",
abstract="AI-Virtual Trainer (AI-VT) is an intelligent tutoring system based on case-based reasoning. AI-VT has been designed to generate personalised, varied, and consistent training sessions for learners. The AI-VT training sessions propose different exercises in regard to a capacity associated with sub-capacities. For example, in the field of training for algorithms, a capacity could be ``Use a control structure alternative'' and an associated sub-capacity could be ``Write a boolean condition''. AI-VT can elaborate a personalised list of exercises for each learner. One of the main requirements and challenges studied in this work is its ability to propose varied training sessions to the same learner for many weeks, which constitutes the challenge studied in our work. Indeed, if the same set of exercises is proposed time after time to learners, they will stop paying attention and lose motivation. Thus, even if the generation of training sessions is based on analogy and must integrate the repetition of some exercises, it also must introduce some diversity and AI-VT must deal with this diversity. In this paper, we have highlighted the fact that the retaining (or capitalisation) phase of CBR is of the utmost importance for diversity, and we have also highlighted that the equilibrium between repetition and variety depends on the abilities learned. This balance has an important impact on the retaining phase of AI-VT.",
isbn="978-3-030-01081-2"
}

@article{BAKUROV2021100913,
title = {Genetic programming for stacked generalization},
journal = {Swarm and Evolutionary Computation},
volume = {65},
pages = {100913},
year = {2021},
issn = {2210-6502},
doi = {https://doi.org/10.1016/j.swevo.2021.100913},
url = {https://www.sciencedirect.com/science/article/pii/S2210650221000742},
author = {Illya Bakurov and Mauro Castelli and Olivier Gau and Francesco Fontanella and Leonardo Vanneschi},
keywords = {Genetic Programming, Stacking, Ensemble Learning, Stacked Generalization},
abstract = {In machine learning, ensemble techniques are widely used to improve the performance of both classification and regression systems. They combine the models generated by different learning algorithms, typically trained on different data subsets or with different parameters, to obtain more accurate models. Ensemble strategies range from simple voting rules to more complex and effective stacked approaches. They are based on adopting a meta-learner, i.e. a further learning algorithm, and are trained on the predictions provided by the single algorithms making up the ensemble. The paper aims at exploiting some of the most recent genetic programming advances in the context of stacked generalization. In particular, we investigate how the evolutionary demes despeciation initialization technique, ϵ-lexicase selection, geometric-semantic operators, and semantic stopping criterion, can be effectively used to improve GP-based systems’ performance for stacked generalization (a.k.a. stacking). The experiments, performed on a broad set of synthetic and real-world regression problems, confirm the effectiveness of the proposed approach.}
}

@article{Liang,
author={Liang Mang and Chang Tianpeng and An Bingxing and Duan Xinghai and Du Lili and Wang Xiaoqiao and Miao Jian and Xu Lingyang and Gao Xue and Zhang Lupei and Li Junya and Gao Huijiang},
Title={A Stacking Ensemble Learning Framework for Genomic Prediction},
Journal={Frontiers in Genetics},
year={2021},
doi ={10.3389/fgene.2021.600040},
PMID={33747037},
PMCID={PMC7969712}
}

@Article{cmc.2023.033417,
AUTHOR = {Jeonghoon Choi and Dongjun Suh and Marc-Oliver Otto},
TITLE = {Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {74},
YEAR = {2023},
NUMBER = {2},
PAGES = {2945--2966},
URL = {http://www.techscience.com/cmc/v74n2/50296},
ISSN = {1546-2226},
ABSTRACT = {Recently, machine learning-based technologies have been developed to automate the classification of wafer map defect patterns during semiconductor manufacturing. The existing approaches used in the wafer map pattern classification include directly learning the image through a convolution neural network and applying the ensemble method after extracting image features. This study aims to classify wafer map defects more effectively and derive robust algorithms even for datasets with insufficient defect patterns. First, the number of defects during the actual process may be limited. Therefore, insufficient data are generated using convolutional auto-encoder (CAE), and the expanded data are verified using the evaluation technique of structural similarity index measure (SSIM). After extracting handcrafted features, a boosted stacking ensemble model that integrates the four base-level classifiers with the extreme gradient boosting classifier as a meta-level classifier is designed and built for training the model based on the expanded data for final prediction. Since the proposed algorithm shows better performance than those of existing ensemble classifiers even for insufficient defect patterns, the results of this study will contribute to improving the product quality and yield of the actual semiconductor manufacturing process.},
DOI = {10.32604/cmc.2023.033417}
}

@ARTICLE{10.3389/fgene.2021.600040,
AUTHOR={Liang, Mang and Chang, Tianpeng and An, Bingxing and Duan, Xinghai and Du, Lili and Wang, Xiaoqiao and Miao, Jian and Xu, Lingyang and Gao, Xue and Zhang, Lupei and Li, Junya and Gao, Huijiang},   
TITLE={A Stacking Ensemble Learning Framework for Genomic Prediction},      
JOURNAL={Frontiers in Genetics},      
VOLUME={12},           
YEAR={2021},      
URL={https://www.frontiersin.org/articles/10.3389/fgene.2021.600040},       
DOI={10.3389/fgene.2021.600040},      
ISSN={1664-8021},   
ABSTRACT={Machine learning (ML) is perhaps the most useful tool for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) is currently unsatisfactory. To improve the genomic predictions, we constructed a stacking ensemble learning framework (SELF), integrating three machine learning methods, to predict genomic estimated breeding values (GEBVs). The present study evaluated the prediction ability of SELF by analyzing three real datasets, with different genetic architecture; comparing the prediction accuracy of SELF, base learners, genomic best linear unbiased prediction (GBLUP) and BayesB. For each trait, SELF performed better than base learners, which included support vector regression (SVR), kernel ridge regression (KRR) and elastic net (ENET). The prediction accuracy of SELF was, on average, 7.70% higher than GBLUP in three datasets. Except for the milk fat percentage (MFP) traits, of the German Holstein dairy cattle dataset, SELF was more robust than BayesB in all remaining traits. Therefore, we believed that SEFL has the potential to be promoted to estimate GEBVs in other animals and plants.}
}

@article{DIDDEN2023338,
title = {Decentralized learning multi-agent system for online machine shop scheduling problem},
journal = {Journal of Manufacturing Systems},
volume = {67},
pages = {338-360},
year = {2023},
issn = {0278-6125},
doi = {https://doi.org/10.1016/j.jmsy.2023.02.004},
url = {https://www.sciencedirect.com/science/article/pii/S0278612523000286},
author = {Jeroen B.H.C. Didden and Quang-Vinh Dang and Ivo J.B.F. Adan},
keywords = {Multi-agent system, Decentralized systems, Learning algorithm, Industry 4.0, Smart manufacturing},
abstract = {Customer profiles have rapidly changed over the past few years, with products being requested with more customization and with lower demand. In addition to the advances in technologies owing to Industry 4.0, manufacturers explore autonomous and smart factories. This paper proposes a decentralized multi-agent system (MAS), including intelligent agents that can respond to their environment autonomously through learning capabilities, to cope with an online machine shop scheduling problem. In the proposed system, agents participate in auctions to receive jobs to process, learn how to bid for jobs correctly, and decide when to start processing a job. The objective is to minimize the mean weighted tardiness of all jobs. In contrast to the existing literature, the proposed MAS is assessed on its learning capabilities, producing novel insights concerning what is relevant for learning, when re-learning is needed, and system response to dynamic events (such as rush jobs, increase in processing time, and machine unavailability). Computational experiments also reveal the outperformance of the proposed MAS to other multi-agent systems by at least 25% and common dispatching rules in mean weighted tardiness, as well as other performance measures.}
}

@article{REZAEI20221,
title = {A Biased Inferential Naivety learning model for a network of agents},
journal = {Cognitive Systems Research},
volume = {76},
pages = {1-12},
year = {2022},
issn = {1389-0417},
doi = {https://doi.org/10.1016/j.cogsys.2022.07.001},
url = {https://www.sciencedirect.com/science/article/pii/S1389041722000298},
author = {Zeinab Rezaei and Saeed Setayeshi and Ebrahim Mahdipour},
keywords = {Bayesian decision making, Heuristic method, Inferential naivety assumption, Observational learning, Social learning},
abstract = {We propose a Biased Inferential Naivety social learning model. In this model, a group of agents tries to determine the true state of the world and make the best possible decisions. The agents have limited computational abilities. They receive noisy private signals about the true state and observe the history of their neighbors' decisions. The proposed model is rooted in the Bayesian method but avoids the complexity of fully Bayesian inference. In our model, the role of knowledge obtained from social observations is separated from the knowledge obtained from private observations. Therefore, the Bayesian inferences on social observations are approximated using inferential naivety assumption, while purely Bayesian inferences are made on private observations. The reduction of herd behavior is another innovation of the proposed model. This advantage is achieved by reducing the effect of social observations on agents' beliefs over time. Therefore, all the agents learn the truth, and the correct consensus is achieved effectively. In this model, using two cognitive biases, there is heterogeneity in agents' behaviors. Therefore, the growth of beliefs and the learning speed can be improved in different situations. Several Monte Carlo simulations confirm the features of the proposed model. The conditions under which the proposed model leads to asymptotic learning are proved.}
}

@article{KAMALI2023110242,
title = {An immune inspired multi-agent system for dynamic multi-objective optimization},
journal = {Knowledge-Based Systems},
volume = {262},
pages = {110242},
year = {2023},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2022.110242},
url = {https://www.sciencedirect.com/science/article/pii/S0950705122013387},
author = {Seyed Ruhollah Kamali and Touraj Banirostam and Homayun Motameni and Mohammad Teshnehlab},
keywords = {Immune inspired multi-agent system, Dynamic multi-objective optimization, Severe and frequent changes},
abstract = {In this research, an immune inspired multi-agent system (IMAS) is proposed to solve optimization problems in dynamic and multi-objective environments. The proposed IMAS uses artificial immune system metaphors to shape the local behaviors of agents to detect environmental changes, generate Pareto optimal solutions, and react to the dynamics of the problem environment. Apart from that, agents enhance their adaptive capacity in dealing with environmental changes to find the global optimum, with a hierarchical structure without any central control. This study used a combination of diversity-, multi-population- and memory-based approaches to perform better in multi-objective environments with severe and frequent changes. The proposed IMAS is compared with six state-of-the-art algorithms on various benchmark problems. The results indicate its superiority in many of the experiments.}
}

@article{ZHANG2023110564,
title = {A novel human learning optimization algorithm with Bayesian inference learning},
journal = {Knowledge-Based Systems},
volume = {271},
pages = {110564},
year = {2023},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2023.110564},
url = {https://www.sciencedirect.com/science/article/pii/S0950705123003143},
author = {Pinggai Zhang and Ling Wang and Zixiang Fei and Lisheng Wei and Minrui Fei and Muhammad Ilyas Menhas},
keywords = {Human learning optimization, Meta-heuristic, Bayesian inference, Bayesian inference learning, Individual learning, Social learning},
abstract = {Humans perform Bayesian inference in a wide variety of tasks, which can help people make selection decisions effectively and therefore enhances learning efficiency and accuracy. Inspired by this fact, this paper presents a novel human learning optimization algorithm with Bayesian inference learning (HLOBIL), in which a Bayesian inference learning operator (BILO) is developed to utilize the inference strategy for enhancing learning efficiency. The in-depth analysis shows that the proposed BILO can efficiently improve the exploitation ability of the algorithm as it can achieve the optimal values and retrieve the optimal information with the accumulated search information. Besides, the exploration ability of HLOBIL is also strengthened by the inborn characteristics of Bayesian inference. The experimental results demonstrate that the developed HLOBIL is superior to previous HLO variants and other state-of-art algorithms with its improved exploitation and exploration abilities.}
}

@article{HIPOLITO2023103510,
title = {Breaking boundaries: The Bayesian Brain Hypothesis for perception and prediction},
journal = {Consciousness and Cognition},
volume = {111},
pages = {103510},
year = {2023},
issn = {1053-8100},
doi = {https://doi.org/10.1016/j.concog.2023.103510},
url = {https://www.sciencedirect.com/science/article/pii/S1053810023000478},
author = {Inês Hipólito and Michael Kirchhoff},
keywords = {Bayesian Brain Hypothesis, Modularity of the Mind, Cognitive processes, Informational boundaries},
abstract = {This special issue aims to provide a comprehensive overview of the current state of the Bayesian Brain Hypothesis and its standing across neuroscience, cognitive science and the philosophy of cognitive science. By gathering cutting-edge research from leading experts, this issue seeks to showcase the latest advancements in our understanding of the Bayesian brain, as well as its potential implications for future research in perception, cognition, and motor control. A special focus to achieve this aim is adopted in this special issue, as it seeks to explore the relation between two seemingly incompatible frameworks for the understanding of cognitive structure and function: the Bayesian Brain Hypothesis and the Modularity Theory of the Mind. In assessing the compatibility between these theories, the contributors to this special issue open up new pathways of thinking and advance our understanding of cognitive processes.}
}

@article{LI2023424,
title = {Multi-agent evolution reinforcement learning method for machining parameters optimization based on bootstrap aggregating graph attention network simulated environment},
journal = {Journal of Manufacturing Systems},
volume = {67},
pages = {424-438},
year = {2023},
issn = {0278-6125},
doi = {https://doi.org/10.1016/j.jmsy.2023.02.015},
url = {https://www.sciencedirect.com/science/article/pii/S0278612523000390},
author = {Weiye Li and Songping He and Xinyong Mao and Bin Li and Chaochao Qiu and Jinwen Yu and Fangyu Peng and Xin Tan},
keywords = {Surface roughness, Cutting efficiency, Machining parameters optimization, Graph attention network, Multi-agent reinforcement learning, Evolutionary learning},
abstract = {Improving machining quality and production efficiency is the focus of the manufacturing industry. How to obtain efficient machining parameters under multiple constraints such as machining quality is a severe challenge for manufacturing industry. In this paper, a multi-agent evolutionary reinforcement learning method (MAERL) is proposed to optimize the machining parameters for high quality and high efficiency machining by combining the graph neural network and reinforcement learning. Firstly, a bootstrap aggregating graph attention network (Bagging-GAT) based roughness estimation method for machined surface is proposed, which combines the structural knowledge between machining parameters and vibration features. Secondly, a mathematical model of machining parameters optimization problem is established, which is formalized into Markov decision process (MDP), and a multi-agent reinforcement learning method is proposed to solve the MDP problem, and evolutionary learning is introduced to improve the stability of multi-agent training. Finally, a series of experiments were carried out on the commutator production line, and the results show that the proposed Bagging-GAT-based method can improve the prediction effect by about 25% in the case of small samples, and the MAERL-based optimization method can better deal with the coupling problem of reward function in the optimization process. Compared with the classical optimization method, the optimization effect is improved by 13% and a lot of optimization time is saved.}
}

@inproceedings{10.1145/3290605.3300912,
author = {Kim, Yea-Seul and Walls, Logan A. and Krafft, Peter and Hullman, Jessica},
title = {A Bayesian Cognition Approach to Improve Data Visualization},
year = {2019},
isbn = {9781450359702},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3290605.3300912},
doi = {10.1145/3290605.3300912},
abstract = {People naturally bring their prior beliefs to bear on how they interpret the new information, yet few formal models exist for accounting for the influence of users' prior beliefs in interactions with data presentations like visualizations. We demonstrate a Bayesian cognitive model for understanding how people interpret visualizations in light of prior beliefs and show how this model provides a guide for improving visualization evaluation. In a first study, we show how applying a Bayesian cognition model to a simple visualization scenario indicates that people's judgments are consistent with a hypothesis that they are doing approximate Bayesian inference. In a second study, we evaluate how sensitive our observations of Bayesian behavior are to different techniques for eliciting people subjective distributions, and to different datasets. We find that people don't behave consistently with Bayesian predictions for large sample size datasets, and this difference cannot be explained by elicitation technique. In a final study, we show how normative Bayesian inference can be used as an evaluation framework for visualizations, including of uncertainty.},
booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems},
pages = {1–14},
numpages = {14},
keywords = {bayesian cognition, uncertainty elicitation, visualization},
location = {Glasgow, Scotland Uk},
series = {CHI '19}
}

@article{DYER2024104827,
title = {Black-box Bayesian inference for agent-based models},
journal = {Journal of Economic Dynamics and Control},
volume = {161},
pages = {104827},
year = {2024},
issn = {0165-1889},
doi = {https://doi.org/10.1016/j.jedc.2024.104827},
url = {https://www.sciencedirect.com/science/article/pii/S0165188924000198},
author = {Joel Dyer and Patrick Cannon and J. Doyne Farmer and Sebastian M. Schmon},
keywords = {Agent-based models, Bayesian inference, Neural networks, Parameter estimation, Simulation-based inference, Time series},
abstract = {Simulation models, in particular agent-based models, are gaining popularity in economics and the social sciences. The considerable flexibility they offer, as well as their capacity to reproduce a variety of empirically observed behaviours of complex systems, give them broad appeal, and the increasing availability of cheap computing power has made their use feasible. Yet a widespread adoption in real-world modelling and decision-making scenarios has been hindered by the difficulty of performing parameter estimation for such models. In general, simulation models lack a tractable likelihood function, which precludes a straightforward application of standard statistical inference techniques. A number of recent works have sought to address this problem through the application of likelihood-free inference techniques, in which parameter estimates are determined by performing some form of comparison between the observed data and simulation output. However, these approaches are (a) founded on restrictive assumptions, and/or (b) typically require many hundreds of thousands of simulations. These qualities make them unsuitable for large-scale simulations in economics and the social sciences, and can cast doubt on the validity of these inference methods in such scenarios. In this paper, we investigate the efficacy of two classes of simulation-efficient black-box approximate Bayesian inference methods that have recently drawn significant attention within the probabilistic machine learning community: neural posterior estimation and neural density ratio estimation. We present a number of benchmarking experiments in which we demonstrate that neural network-based black-box methods provide state of the art parameter inference for economic simulation models, and crucially are compatible with generic multivariate or even non-Euclidean time-series data. In addition, we suggest appropriate assessment criteria for use in future benchmarking of approximate Bayesian inference procedures for simulation models in economics and the social sciences.}
}

@Article{Nikpour2021,
author={Nikpour, Hoda
and Aamodt, Agnar},
title={Inference and reasoning in a Bayesian knowledge-intensive CBR system},
journal={Progress in Artificial Intelligence},
year={2021},
month={Mar},
day={01},
volume={10},
number={1},
pages={49-63},
abstract={This paper presents the inference and reasoning methods in a Bayesian supported knowledge-intensive case-based reasoning (CBR) system called BNCreek. The inference and reasoning process in this system is a combination of three methods. The semantic network inference methods and the CBR method are employed to handle the difficulties of inferencing and reasoning in uncertain domains. The Bayesian network inference methods are employed to make the process more accurate. An experiment from oil well drilling as a complex and uncertain application domain is conducted. The system is evaluated against expert estimations and compared with seven other corresponding systems. The normalized discounted cumulative gain (NDCG) as a rank-based metric, the weighted error (WE), and root-square error (RSE) as the statistical metrics are employed to evaluate different aspects of the system capabilities. The results show the efficiency of the developed inference and reasoning methods.},
issn={2192-6360},
doi={10.1007/s13748-020-00223-1},
url={https://doi.org/10.1007/s13748-020-00223-1}
}

@article{PRESCOTT2024112577,
title = {Efficient multifidelity likelihood-free Bayesian inference with adaptive computational resource allocation},
journal = {Journal of Computational Physics},
volume = {496},
pages = {112577},
year = {2024},
issn = {0021-9991},
doi = {https://doi.org/10.1016/j.jcp.2023.112577},
url = {https://www.sciencedirect.com/science/article/pii/S0021999123006721},
author = {Thomas P. Prescott and David J. Warne and Ruth E. Baker},
keywords = {Likelihood-free Bayesian inference, Multifidelity approaches},
abstract = {Likelihood-free Bayesian inference algorithms are popular methods for inferring the parameters of complex stochastic models with intractable likelihoods. These algorithms characteristically rely heavily on repeated model simulations. However, whenever the computational cost of simulation is even moderately expensive, the significant burden incurred by likelihood-free algorithms leaves them infeasible for many practical applications. The multifidelity approach has been introduced in the context of approximate Bayesian computation to reduce the simulation burden of likelihood-free inference without loss of accuracy, by using the information provided by simulating computationally cheap, approximate models in place of the model of interest. In this work we demonstrate that multifidelity techniques can be applied in the general likelihood-free Bayesian inference setting. Analytical results on the optimal allocation of computational resources to simulations at different levels of fidelity are derived, and subsequently implemented practically. We provide an adaptive multifidelity likelihood-free inference algorithm that learns the relationships between models at different fidelities and adapts resource allocation accordingly, and demonstrate that this algorithm produces posterior estimates with near-optimal efficiency.}
}

@article{RISTIC202030,
title = {A tutorial on uncertainty modeling for machine reasoning},
journal = {Information Fusion},
volume = {55},
pages = {30-44},
year = {2020},
issn = {1566-2535},
doi = {https://doi.org/10.1016/j.inffus.2019.08.001},
url = {https://www.sciencedirect.com/science/article/pii/S1566253519301976},
author = {Branko Ristic and Christopher Gilliam and Marion Byrne and Alessio Benavoli},
keywords = {Information fusion, Uncertainty, Imprecision, Model based classification, Bayesian, Random sets, Belief function theory, Possibility functions, Imprecise probability},
abstract = {Increasingly we rely on machine intelligence for reasoning and decision making under uncertainty. This tutorial reviews the prevalent methods for model-based autonomous decision making based on observations and prior knowledge, primarily in the context of classification. Both observations and the knowledge-base available for reasoning are treated as being uncertain. Accordingly, the central themes of this tutorial are quantitative modeling of uncertainty, the rules required to combine such uncertain information, and the task of decision making under uncertainty. The paper covers the main approaches to uncertain knowledge representation and reasoning, in particular, Bayesian probability theory, possibility theory, reasoning based on belief functions and finally imprecise probability theory. The main feature of the tutorial is that it illustrates various approaches with several testing scenarios, and provides MATLAB solutions for them as a supplementary material for an interested reader.}
}

@article{CICIRELLO2022108619,
title = {Machine learning based optimization for interval uncertainty propagation},
journal = {Mechanical Systems and Signal Processing},
volume = {170},
pages = {108619},
year = {2022},
issn = {0888-3270},
doi = {https://doi.org/10.1016/j.ymssp.2021.108619},
url = {https://www.sciencedirect.com/science/article/pii/S0888327021009493},
author = {Alice Cicirello and Filippo Giunta},
keywords = {Bounded uncertainty, Bayesian optimization, Expensive-to-evaluate deterministic computer models, Gaussian process, Communicating uncertainty},
abstract = {Two non-intrusive uncertainty propagation approaches are proposed for the performance analysis of engineering systems described by expensive-to-evaluate deterministic computer models with parameters defined as interval variables. These approaches employ a machine learning based optimization strategy, the so-called Bayesian optimization, for evaluating the upper and lower bounds of a generic response variable over the set of possible responses obtained when each interval variable varies independently over its range. The lack of knowledge caused by not evaluating the response function for all the possible combinations of the interval variables is accounted for by developing a probabilistic description of the response variable itself by using a Gaussian Process regression model. An iterative procedure is developed for selecting a small number of simulations to be evaluated for updating this statistical model by using well-established acquisition functions and to assess the response bounds. In both approaches, an initial training dataset is defined. While one approach builds iteratively two distinct training datasets for evaluating separately the upper and lower bounds of the response variable, the other one builds iteratively a single training dataset. Consequently, the two approaches will produce different bound estimates at each iteration. The upper and lower response bounds are expressed as point estimates obtained from the mean function of the posterior distribution. Moreover, a confidence interval on each estimate is provided for effectively communicating to engineers when these estimates are obtained at a combination of the interval variables for which no deterministic simulation has been run. Finally, two metrics are proposed to define conditions for assessing if the predicted bound estimates can be considered satisfactory. The applicability of these two approaches is illustrated with two numerical applications, one focusing on vibration and the other on vibro-acoustics.}
}

@INPROCEEDINGS{9278071,
  author={Petit, Maxime and Dellandrea, Emmanuel and Chen, Liming},
  booktitle={2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)}, 
  title={Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction}, 
  year={2020},
  volume={},
  number={},
  pages={1-8},
  keywords={Optimization;Robots;Task analysis;Bayes methods;Visualization;Service robots;Cognition;developmental robotics;long-term memory;meta learning;hyperparmeters automatic optimization;case-based reasoning},
  doi={10.1109/ICDL-EpiRob48136.2020.9278071}
}

@article{LI2023477,
title = {Hierarchical and partitioned planning strategy for closed-loop devices in low-voltage distribution network based on improved KMeans partition method},
journal = {Energy Reports},
volume = {9},
pages = {477-485},
year = {2023},
note = {2022 The 3rd International Conference on Power and Electrical Engineering},
issn = {2352-4847},
doi = {https://doi.org/10.1016/j.egyr.2023.05.161},
url = {https://www.sciencedirect.com/science/article/pii/S2352484723009137},
author = {Jingqi Li and Junlin Li and Dan Wang and Chengxiong Mao and Zhitao Guan and Zhichao Liu and Miaomiao Du and Yuanzhuo Qi and Lexiang Wang and Wenge Liu and Pengfei Tang},
keywords = {Closed-loop device, Distribution network partition, Device planning, Hierarchical planning, Improved KMeans partition method},
abstract = {To improve the reliability of power supply, this paper proposes a hierarchical and partitioned planning strategy for closed-loop devices in low-voltage distribution network. Based on the geographic location and load situation of the distribution network area, an improved KMeans partition method is used to partition the area in the upper layer. In the lower layer, an intelligent algorithm is adopted to decide the numbers and placement locations of mobile low-voltage contact boxes and mobile seamless closed-loop load transfer devices in each partition with the goal of the highest closed-loop safety, the greatest improvement in annual power outage amount and the lowest cost. Finally, the feasibility and effectiveness of the proposed strategy are proved by an example.}
}

@article{SAXENA2024100838,
title = {Hybrid KNN-SVM machine learning approach for solar power forecasting},
journal = {Environmental Challenges},
volume = {14},
pages = {100838},
year = {2024},
issn = {2667-0100},
doi = {https://doi.org/10.1016/j.envc.2024.100838},
url = {https://www.sciencedirect.com/science/article/pii/S2667010024000040},
author = {Nishant Saxena and Rahul Kumar and Yarrapragada K S S Rao and Dilbag Singh Mondloe and Nishikant Kishor Dhapekar and Abhishek Sharma and Anil Singh Yadav},
keywords = {Solar power forecasting, Hybrid model, KNN, Optimization, Solar energy, SVM},
abstract = {Predictions about solar power will have a significant impact on large-scale renewable energy plants. Photovoltaic (PV) power generation forecasting is particularly sensitive to measuring the uncertainty in weather conditions. Although several conventional techniques like long short-term memory (LSTM), support vector machine (SVM), etc. are available, but due to some restrictions, their application is limited. To enhance the precision of forecasting solar power from solar farms, a hybrid machine learning model that includes blends of the K-Nearest Neighbor (KNN) machine learning technique with the SVM to increase reliability for power system operators is proposed in this investigation. The conventional LSTM technique is also implemented to compare the performance of the proposed hybrid technique. The suggested hybrid model is improved by the use of structural diversity and data diversity in KNN and SVM, respectively. For the solar power predictions, the suggested method was tested on the Jodhpur real-time series dataset obtained from the data centers of weather stations using Meteonorm. The data set includes metrics such as Hourly Average Temperature (HAT), Hourly Total Sunlight Duration (HTSD), Hourly Total Global Solar Radiation (HTGSR), and Hourly Total Photovoltaic Energy Generation (HTPEG). The collated data has been segmented into training data, validation data, and testing data. Furthermore, the proposed technique performed better when evaluated on the three performance indices, viz., accuracy, sensitivity, and specificity. Compared with the conventional LSTM technique, the hybrid technique improved the prediction with 98\% accuracy.}
}

@article{RAKESH2023100898,
title = {Moving object detection using modified GMM based background subtraction},
journal = {Measurement: Sensors},
volume = {30},
pages = {100898},
year = {2023},
issn = {2665-9174},
doi = {https://doi.org/10.1016/j.measen.2023.100898},
url = {https://www.sciencedirect.com/science/article/pii/S2665917423002349},
author = {S. Rakesh and Nagaratna P. Hegde and M. {Venu Gopalachari} and D. Jayaram and Bhukya Madhu and Mohd Abdul Hameed and Ramdas Vankdothu and L.K. {Suresh Kumar}},
keywords = {Background subtraction, Gaussian mixture models, Intelligent video surveillance, Object detection},
abstract = {Academics have become increasingly interested in creating cutting-edge technologies to enhance Intelligent Video Surveillance (IVS) performance in terms of accuracy, speed, complexity, and deployment. It has been noted that precise object detection is the only way for IVS to function well in higher level applications including event interpretation, tracking, classification, and activity recognition. Through the use of cutting-edge techniques, the current study seeks to improve the performance accuracy of object detection techniques based on Gaussian Mixture Models (GMM). It is achieved by developing crucial phases in the object detecting process. In this study, it is discussed how to model each pixel as a mixture of Gaussians and how to update the model using an online k-means approximation. The adaptive mixture model's Gaussian distributions are then analyzed to identify which ones are more likely to be the product of a background process. Each pixel is categorized according to whether the background model is thought to include the Gaussian distribution that best depicts it.}
}

@article{JIAO2022540,
title = {Interpretable fuzzy clustering using unsupervised fuzzy decision trees},
journal = {Information Sciences},
volume = {611},
pages = {540-563},
year = {2022},
issn = {0020-0255},
doi = {https://doi.org/10.1016/j.ins.2022.08.077},
url = {https://www.sciencedirect.com/science/article/pii/S0020025522009872},
author = {Lianmeng Jiao and Haoyu Yang and Zhun-ga Liu and Quan Pan},
keywords = {Fuzzy clustering, Interpretable clustering, Unsupervised decision tree, Cluster merging},
abstract = {In clustering process, fuzzy partition performs better than hard partition when the boundaries between clusters are vague. Whereas, traditional fuzzy clustering algorithms produce less interpretable results, limiting their application in security, privacy, and ethics fields. To that end, this paper proposes an interpretable fuzzy clustering algorithm—fuzzy decision tree-based clustering which combines the flexibility of fuzzy partition with the interpretability of the decision tree. We constructed an unsupervised multi-way fuzzy decision tree to achieve the interpretability of clustering, in which each cluster is determined by one or several paths from the root to leaf nodes. The proposed algorithm comprises three main modules: feature and cutting point-selection, node fuzzy splitting, and cluster merging. The first two modules are repeated to generate an initial unsupervised decision tree, and the final module is designed to combine similar leaf nodes to form the final compact clustering model. Our algorithm optimizes an internal clustering validation metric to automatically determine the number of clusters without their initial positions. The synthetic and benchmark datasets were used to test the performance of the proposed algorithm. Furthermore, we provided two examples demonstrating its interest in solving practical problems.}
}

@article{ARNAUGONZALEZ2023101516,
title = {A methodological approach to enable natural language interaction in an Intelligent Tutoring System},
journal = {Computer Speech and Language},
volume = {81},
pages = {101516},
year = {2023},
issn = {0885-2308},
doi = {https://doi.org/10.1016/j.csl.2023.101516},
url = {https://www.sciencedirect.com/science/article/pii/S0885230823000359},
author = {Pablo Arnau-González and Miguel Arevalillo-Herráez and Romina Albornoz-De Luise and David Arnau},
keywords = {Intelligent tutoring systems (ITS), Interactive learning environments (ILE), Conversational agents, Rasa, Natural language understanding (NLU), Natural language processing (NLP)},
abstract = {In this paper, we present and evaluate the recent incorporation of a conversational agent into an Intelligent Tutoring System (ITS), using the open-source machine learning framework Rasa. Once it has been appropriately trained, this tool is capable of identifying the intention of a given text input and extracting the relevant entities related to the message content. We describe both the generation of a realistic training set in Spanish language that enables the creation of the required Natural Language Understanding (NLU) models and the evaluation of the resulting system. For the generation of the training set, we have followed a methodology that can be easily exported to other ITS. The model evaluation shows that the conversational agent can correctly identify the majority of the user intents, reporting an f1-score above 95%, and cooperate with the ITS to produce a consistent dialogue flow that makes interaction more natural.}
}

@article{MAO20224065,
title = {An Exploratory Approach to Intelligent Quiz Question Recommendation},
journal = {Procedia Computer Science},
volume = {207},
pages = {4065-4074},
year = {2022},
note = {Knowledge-Based and Intelligent Information and Engineering Systems: Proceedings of the 26th International Conference KES2022},
issn = {1877-0509},
doi = {https://doi.org/10.1016/j.procs.2022.09.469},
url = {https://www.sciencedirect.com/science/article/pii/S1877050922013631},
author = {Kejie Mao and Qiwen Dong and Ye Wang and Daocheng Honga},
keywords = {question recommendation, two-sided recommender systems, reinforcement learning, intelligent tutoring},
abstract = {With the rapid advancement of ICT, the digital transformation on education is greatly accelerating in various applications. As a particularly prominent application of digital education, quiz question recommendation is playing a vital role in precision teaching, smart tutoring, and personalized learning. However, the looming challenge of quiz question recommender for students is to satisfy the question diversity demands for students ZPD (the zone of proximal development) stage dynamically online. Therefore, we propose to formalize quiz question recommendation with a novel approach of reinforcement learning based two-sided recommender system. We develop a recommendation framework RTR (Reinforcement-Learning based Two-sided Recommender Systems) for taking into account the interests of both sides of the system, learning and adapting to those interests in real time, and resulting in more satisfactory recommended content. This established recommendation framework captures question characters and student dynamic preferences by considering the emergence of both sides of the system, and it yields a better learning experience in the context of practical quiz question generation.}
}

@article{CLEMENTE2022118171,
title = {A proposal for an adaptive Recommender System based on competences and ontologies},
journal = {Expert Systems with Applications},
volume = {208},
pages = {118171},
year = {2022},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2022.118171},
url = {https://www.sciencedirect.com/science/article/pii/S0957417422013392},
author = {Julia Clemente and Héctor Yago and Javier {de Pedro-Carracedo} and Javier Bueno},
keywords = {Recommender system, , Ontology network, Methodological development, Student modeling},
abstract = {Context:
Competences represent an interesting pedagogical support in many processes like diagnosis or recommendation. From these, it is possible to infer information about the progress of the student to provide help targeted both, trainers who must make adaptive tutoring decisions for each learner, and students to detect and correct their learning weaknesses. For the correct development of any of these tasks, it is important to have a suitable student model that allows the representation of the most significant information possible about the student. Additionally, it would be very advantageous for this modeling to incorporate mechanisms from which it would be possible to infer more information about the student’s state of knowledge.
Objective:
To facilitate this goal, in this paper a new approach to develop an adaptive competence-based recommender system is proposed.
Method:
We present a methodological development guide as well as a set of ontological and non-ontological resources to develop and adapt the prototype of the proposed recommender system.
Results:
A modular flexible ontology network previously built for this purpose has been extended, which is responsible for recording the instructional design and student information. Furthermore, we describe a case study based on a first aid learning experience to assess the prototype with the proposed methodology.
Conclusions:
We highlight the relevance of flexibility and adaptability in learning modeling and recommendation processes. In order to promote improvement in the personalized learning of students, we present a Recommender System prototype taking advantages of ontologies, with a methodological guide, a broad taxonomy of recommendation criteria and the nature of competences. Future lines of research lines, including a more comprehensive evaluation of the system, will allow us to demonstrate in depth its adaptability according to the characteristics of the student, flexibility and extensibility for its integration in various environments and domains.}
}

@article{https//doi.org/10.1155/2023/2578286,
author = {Li, Linqing and Wang, Zhifeng},
title = {Knowledge Graph-Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness},
journal = {International Journal of Intelligent Systems},
volume = {2023},
number = {1},
pages = {2578286},
doi = {https://doi.org/10.1155/2023/2578286},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1155/2023/2578286},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1155/2023/2578286},
abstract = {In the realm of online tutoring intelligent systems, e-learners are exposed to a substantial volume of learning content. The extraction and organization of exercises and skills hold significant importance in establishing clear learning objectives and providing appropriate exercise recommendations. Presently, knowledge graph-based recommendation algorithms have garnered considerable attention among researchers. However, these algorithms solely consider knowledge graphs with single relationships and do not effectively model exercise-rich features, such as exercise representativeness and informativeness. Consequently, this paper proposes a framework, namely, the Knowledge Graph Importance-Exercise Representativeness and Informativeness Framework, to address these two issues. The framework consists of four intricate components and a novel cognitive diagnosis model called the Neural Attentive Cognitive Diagnosis model to recommend the proper exercises. These components encompass the informativeness component, exercise representation component, knowledge importance component, and exercise representativeness component. The informativeness component evaluates the informational value of each exercise and identifies the candidate exercise set (EC) that exhibits the highest exercise informativeness. Moreover, the exercise representation component utilizes a graph neural network to process student records. The output of the graph neural network serves as the input for exercise-level attention and skill-level attention, ultimately generating exercise embeddings and skill embeddings. Furthermore, the skill embeddings are employed as input for the knowledge importance component. This component transforms a one-dimensional knowledge graph into a multidimensional one through four class relations and calculates skill importance weights based on novelty and popularity. Subsequently, the exercise representativeness component incorporates exercise weight knowledge coverage to select exercises from the candidate exercise set for the tested exercise set. Lastly, the cognitive diagnosis model leverages exercise representation and skill importance weights to predict student performance on the test set and estimate their knowledge state. To evaluate the effectiveness of our selection strategy, extensive experiments were conducted on two types of publicly available educational datasets. The experimental results demonstrate that our framework can recommend appropriate exercises to students, leading to improved student performance.},
year = {2023}
}

@inproceedings{badierhal-04092828,
  TITLE = {{Comprendre les usages et effets d'un syst{\`e}me de recommandations p{\'e}dagogiques en contexte d'apprentissage non-formel}},
  AUTHOR = {Badier, Ana{\"e}lle and Lefort, Mathieu and Lefevre, Marie},
  URL = {https://hal.science/hal-04092828},
  BOOKTITLE = {{EIAH'23}},
  ADDRESS = {Brest, France},
  YEAR = {2023},
  MONTH = Jun,
  HAL_ID = {hal-04092828},
  HAL_VERSION = {v1},
}

@article{BADRA2023108920,
title = {Case-based prediction – A survey},
journal = {International Journal of Approximate Reasoning},
volume = {158},
pages = {108920},
year = {2023},
issn = {0888-613X},
doi = {https://doi.org/10.1016/j.ijar.2023.108920},
url = {https://www.sciencedirect.com/science/article/pii/S0888613X23000440},
author = {Fadi Badra and Marie-Jeanne Lesot},
keywords = {Case-based prediction, Analogical transfer, Similarity},
abstract = {This paper clarifies the relation between case-based prediction and analogical transfer. Case-based prediction consists in predicting the outcome associated with a new case directly from its comparison with a set of cases retrieved from a case base, by relying solely on a structured memory and some similarity measures. Analogical transfer is a cognitive process that allows to derive some new information about a target situation by applying a plausible inference principle, according to which if two situations are similar with respect to some criteria, then it is plausible that they are also similar with respect to other criteria. Case-based prediction algorithms are known to apply analogical transfer to make predictions, but the existing approaches are diverse, and developing a unified theory of case-based prediction remains a challenge. In this paper, we show that a common principle underlying case-based prediction methods is that they interpret the plausible inference as a transfer of similarity knowledge from a situation space to an outcome space. Among all potential outcomes, the predicted outcome is the one that optimizes this transfer, i.e., that makes the similarities in the outcome space most compatible with the observed similarities in the situation space. Based on this observation, a systematic analysis of the different theories of case-based prediction is presented, where the approaches are distinguished according to the type of knowledge used to measure the compatibility between the two sets of similarity relations.}
}


@Article{jmse11050890 ,
AUTHOR = {Louvros, Panagiotis and Stefanidis, Fotios and Boulougouris, Evangelos and Komianos, Alexandros and Vassalos, Dracos},
TITLE = {Machine Learning and Case-Based Reasoning for Real-Time Onboard Prediction of the Survivability of Ships},
JOURNAL = {Journal of Marine Science and Engineering},
VOLUME = {11},
YEAR = {2023},
NUMBER = {5},
ARTICLE-NUMBER = {890},
URL = {https://www.mdpi.com/2077-1312/11/5/890},
ISSN = {2077-1312},
ABSTRACT = {The subject of damaged stability has greatly profited from the development of new tools and techniques in recent history. Specifically, the increased computational power and the probabilistic approach have transformed the subject, increasing accuracy and fidelity, hence allowing for a universal application and the inclusion of the most probable scenarios. Currently, all ships are evaluated for their stability and are expected to survive the dangers they will most likely face. However, further advancements in simulations have made it possible to further increase the fidelity and accuracy of simulated casualties. Multiple time domain and, to a lesser extent, Computational Fluid dynamics (CFD) solutions have been suggested as the next “evolutionary” step for damage stability. However, while those techniques are demonstrably more accurate, the computational power to utilize them for the task of probabilistic evaluation is not there yet. In this paper, the authors present a novel approach that aims to serve as a stopgap measure for introducing the time domain simulations in the existing framework. Specifically, the methodology presented serves the purpose of a fast decision support tool which is able to provide information regarding the ongoing casualty utilizing prior knowledge gained from simulations. This work was needed and developed for the purposes of the EU-funded project SafePASS.},
DOI = {10.3390/jmse11050890}
}


@Article{su14031366,
AUTHOR = {Chun, Se-Hak and Jang, Jae-Won},
TITLE = {A New Trend Pattern-Matching Method of Interactive Case-Based Reasoning for Stock Price Predictions},
JOURNAL = {Sustainability},
VOLUME = {14},
YEAR = {2022},
NUMBER = {3},
ARTICLE-NUMBER = {1366},
URL = {https://www.mdpi.com/2071-1050/14/3/1366},
ISSN = {2071-1050},
ABSTRACT = {In this paper, we suggest a new case-based reasoning method for stock price predictions using the knowledge of traders to select similar past patterns among nearest neighbors obtained from a traditional case-based reasoning machine. Thus, this method overcomes the limitation of conventional case-based reasoning, which does not consider how to retrieve similar neighbors from previous patterns in terms of a graphical pattern. In this paper, we show how the proposed method can be used when traders find similar time series patterns among nearest cases. For this, we suggest an interactive prediction system where traders can select similar patterns with individual knowledge among automatically recommended neighbors by case-based reasoning. In this paper, we demonstrate how traders can use their knowledge to select similar patterns using a graphical interface, serving as an exemplar for the target. These concepts are investigated against the backdrop of a practical application involving the prediction of three individual stock prices, i.e., Zoom, Airbnb, and Twitter, as well as the prediction of the Dow Jones Industrial Average (DJIA). The verification of the prediction results is compared with a random walk model based on the RMSE and Hit ratio. The results show that the proposed technique is more effective than the random walk model but it does not statistically surpass the random walk model.},
DOI = {10.3390/su14031366}
}

@Article{fire7040107,
AUTHOR = {Pei, Qiuyan and Jia, Zhichao and Liu, Jia and Wang, Yi and Wang, Junhui and Zhang, Yanqi},
TITLE = {Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning},
JOURNAL = {Fire},
VOLUME = {7},
YEAR = {2024},
NUMBER = {4},
ARTICLE-NUMBER = {107},
URL = {https://www.mdpi.com/2571-6255/7/4/107},
ISSN = {2571-6255},
ABSTRACT = {Accurate prediction of the coal spontaneous combustion hazard grades is of great significance to ensure the safe production of coal mines. However, traditional coal temperature prediction models have low accuracy and do not predict the coal spontaneous combustion hazard grades. In order to accurately predict coal spontaneous combustion hazard grades, a prediction model of coal spontaneous combustion based on principal component analysis (PCA), case-based reasoning (CBR), fuzzy clustering (FM), and the snake optimization (SO) algorithm was proposed in this manuscript. Firstly, based on the change rule of the concentration of signature gases in the process of coal warming, a new method of classifying the risk of spontaneous combustion of coal was established. Secondly, MeanRadius-SMOTE was adopted to balance the data structure. The weights of the prediction indicators were calculated through PCA to enhance the prediction precision of the CBR model. Then, by employing FM in the case base, the computational cost of CBR was reduced and its computational efficiency was improved. The SO algorithm was used to determine the hyperparameters in the PCA-FM-CBR model. In addition, multiple comparative experiments were conducted to verify the superiority of the model proposed in this manuscript. The results indicated that SO-PCA-FM-CBR possesses good prediction performance and also improves computational efficiency. Finally, the authors of this manuscript adopted the Random Balance Designs—Fourier Amplitude Sensitivity Test (RBD-FAST) to explain the output of the model and analyzed the global importance of input variables. The results demonstrated that CO is the most important variable affecting the coal spontaneous combustion hazard grades.},
DOI = {10.3390/fire7040107}
}

﻿@Article{Desmarais2012,
author={Desmarais, Michel C.
and Baker, Ryan S. J. d.},
title={A review of recent advances in learner and skill modeling in intelligent learning environments},
journal={User Modeling and User-Adapted Interaction},
year={2012},
month={Apr},
day={01},
volume={22},
number={1},
pages={9-38},
abstract={In recent years, learner models have emerged from the research laboratory and research classrooms into the wider world. Learner models are now embedded in real world applications which can claim to have thousands, or even hundreds of thousands, of users. Probabilistic models for skill assessment are playing a key role in these advanced learning environments. In this paper, we review the learner models that have played the largest roles in the success of these learning environments, and also the latest advances in the modeling and assessment of learner skills. We conclude by discussing related advancements in modeling other key constructs such as learner motivation, emotional and attentional state, meta-cognition and self-regulated learning, group learning, and the recent movement towards open and shared learner models.},
issn={1573-1391},
doi={10.1007/s11257-011-9106-8},
url={https://doi.org/10.1007/s11257-011-9106-8}
}

@article{Eide,
title={Dynamic slate recommendation with gated recurrent units and Thompson sampling},
author={Eide, Simen and Leslie, David S. and Frigessi, Arnoldo},
language={English},
type={article},
volume = {36},
year = {2022},
issn = {1573-756X},
doi = {https://doi.org/10.1007/s10618-022-00849-w},
url = {https://doi.org/10.1007/s10618-022-00849-w},
abstract={We consider the problem of recommending relevant content to users of an internet platform in the form of lists of items, called slates. We introduce a variational Bayesian Recurrent Neural Net recommender system that acts on time series of interactions between the internet platform and the user, and which scales to real world industrial situations. The recommender system is tested both online on real users, and on an offline dataset collected from a Norwegian web-based marketplace, FINN.no, that is made public for research. This is one of the first publicly available datasets which includes all the slates that are presented to users as well as which items (if any) in the slates were clicked on. Such a data set allows us to move beyond the common assumption that implicitly assumes that users are considering all possible items at each interaction. Instead we build our likelihood using the items that are actually in the slate, and evaluate the strengths and weaknesses of both approaches theoretically and in experiments. We also introduce a hierarchical prior for the item parameters based on group memberships. Both item parameters and user preferences are learned probabilistically. Furthermore, we combine our model with bandit strategies to ensure learning, and introduce ‘in-slate Thompson sampling’ which makes use of the slates to maximise explorative opportunities. We show experimentally that explorative recommender strategies perform on par or above their greedy counterparts. Even without making use of exploration to learn more effectively, click rates increase simply because of improved diversity in the recommended slates.}
}

@InProceedings{10.1007/978-3-031-09680-8_14,
author={Sablayrolles, Louis
and Lefevre, Marie
and Guin, Nathalie
and Broisin, Julien},
editor={Crossley, Scott
and Popescu, Elvira},
title={Design and Evaluation of a Competency-Based Recommendation Process},
booktitle={Intelligent Tutoring Systems},
year={2022},
publisher={Springer International Publishing},
address={Cham},
pages={148--160},
abstract={The purpose of recommending activities to learners is to provide them with resources adapted to their needs, to facilitate the learning process. However, when teachers face a large number of students, it is difficult for them to recommend a personalized list of resources to each learner. In this paper, we are interested in the design of a system that automatically recommends resources to learners using their cognitive profile expressed in terms of competencies, but also according to a specific strategy defined by teachers. Our contributions relate to (1) a competency-based pedagogical strategy allowing to express the teacher's expertise, and (2) a recommendation process based on this strategy. This process has been experimented and assessed with students learning Shell programming in a first-year computer science degree. The first results show that (i) the items selected by our system from the set of possible items were relevant according to the experts; (ii) our system provided recommendations in a reasonable time; (iii) the recommendations were consulted by the learners but lacked usability.},
isbn={978-3-031-09680-8}
}

@inproceedings{10.1145/3578337.3605122,
author = {Xu, Shuyuan and Ge, Yingqiang and Li, Yunqi and Fu, Zuohui and Chen, Xu and Zhang, Yongfeng},
title = {Causal Collaborative Filtering},
year = {2023},
isbn = {9798400700736},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3578337.3605122},
doi = {10.1145/3578337.3605122},
abstract = {Many of the traditional recommendation algorithms are designed based on the fundamental idea of mining or learning correlative patterns from data to estimate the user-item correlative preference. However, pure correlative learning may lead to Simpson's paradox in predictions, and thus results in sacrificed recommendation performance. Simpson's paradox is a well-known statistical phenomenon, which causes confusions in statistical conclusions and ignoring the paradox may result in inaccurate decisions. Fortunately, causal and counterfactual modeling can help us to think outside of the observational data for user modeling and personalization so as to tackle such issues. In this paper, we propose Causal Collaborative Filtering (CCF) --- a general framework for modeling causality in collaborative filtering and recommendation. We provide a unified causal view of CF and mathematically show that many of the traditional CF algorithms are actually special cases of CCF under simplified causal graphs. We then propose a conditional intervention approach for do-operations so that we can estimate the user-item causal preference based on the observational data. Finally, we further propose a general counterfactual constrained learning framework for estimating the user-item preferences. Experiments are conducted on two types of real-world datasets---traditional and randomized trial data---and results show that our framework can improve the recommendation performance and reduce the Simpson's paradox problem of many CF algorithms.},
booktitle = {Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval},
pages = {235–245},
numpages = {11},
keywords = {recommender systems, counterfactual reasoning, collaborative filtering, causal analysis, Simpson's paradox},
location = {Taipei, Taiwan},
series = {ICTIR '23}
}

@inproceedings{10.1145/3583780.3615048,
author = {Zhu, Zheqing and Van Roy, Benjamin},
title = {Scalable Neural Contextual Bandit for Recommender Systems},
year = {2023},
isbn = {9798400701245},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3583780.3615048},
doi = {10.1145/3583780.3615048},
abstract = {High-quality recommender systems ought to deliver both innovative and relevant content through effective and exploratory interactions with users. Yet, supervised learning-based neural networks, which form the backbone of many existing recommender systems, only leverage recognized user interests, falling short when it comes to efficiently uncovering unknown user preferences. While there has been some progress with neural contextual bandit algorithms towards enabling online exploration through neural networks, their onerous computational demands hinder widespread adoption in real-world recommender systems. In this work, we propose a scalable sample-efficient neural contextual bandit algorithm for recommender systems. To do this, we design an epistemic neural network architecture, Epistemic Neural Recommendation (ENR), that enables Thompson sampling at a large scale. In two distinct large-scale experiments with real-world tasks, ENR significantly boosts click-through rates and user ratings by at least 9\% and 6\% respectively compared to state-of-the-art neural contextual bandit algorithms. Furthermore, it achieves equivalent performance with at least 29\% fewer user interactions compared to the best-performing baseline algorithm. Remarkably, while accomplishing these improvements, ENR demands orders of magnitude fewer computational resources than neural contextual bandit baseline algorithms.},
booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
pages = {3636–3646},
numpages = {11},
keywords = {contextual bandits, decision making under uncertainty, exploration vs exploitation, recommender systems, reinforcement learning},
location = {Birmingham, United Kingdom},
series = {CIKM '23}
}

@ARTICLE{10494875,
  author={Ghoorchian, Saeed and Kortukov, Evgenii and Maghsudi, Setareh},
  journal={IEEE Open Journal of Signal Processing}, 
  title={Non-Stationary Linear Bandits With Dimensionality Reduction for Large-Scale Recommender Systems}, 
  year={2024},
  volume={5},
  number={},
  pages={548-558},
  keywords={Vectors;Recommender systems;Decision making;Runtime;Signal processing algorithms;Covariance matrices;Robustness;Decision-making;multi-armed bandit;non-stationary environment;online learning;recommender systems},
  doi={10.1109/OJSP.2024.3386490}
}

@article{GIANNIKIS2024111752,
title = {Reinforcement learning for addressing the cold-user problem in recommender systems},
journal = {Knowledge-Based Systems},
volume = {294},
pages = {111752},
year = {2024},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2024.111752},
url = {https://www.sciencedirect.com/science/article/pii/S0950705124003873},
author = {Stelios Giannikis and Flavius Frasincar and David Boekestijn},
keywords = {Recommender systems, Reinforcement learning, Active learning, Cold-user problem},
abstract = {Recommender systems are widely used in webshops because of their ability to provide users with personalized recommendations. However, the cold-user problem (i.e., recommending items to new users) is an important issue many webshops face. With the recent General Data Protection Regulation in Europe, the use of additional user information such as demographics is not possible without the user’s explicit consent. Several techniques have been proposed to solve the cold-user problem. Many of these techniques utilize Active Learning (AL) methods, which let cold users rate items to provide better recommendations for them. In this research, we propose two novel approaches that combine reinforcement learning with AL to elicit the users’ preferences and provide them with personalized recommendations. We compare reinforcement learning approaches that are either AL-based or item-based, where the latter predicts users’ ratings of an item by using their ratings of similar items. Differently than many of the existing approaches, this comparison is made based on implicit user information. Using a large real-world dataset, we show that the item-based strategy is more accurate than the AL-based strategy as well as several existing AL strategies.}
}

@article{IFTIKHAR2024121541,
title = {A reinforcement learning recommender system using bi-clustering and Markov Decision Process},
journal = {Expert Systems with Applications},
volume = {237},
pages = {121541},
year = {2024},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2023.121541},
url = {https://www.sciencedirect.com/science/article/pii/S0957417423020432},
author = {Arta Iftikhar and Mustansar Ali Ghazanfar and Mubbashir Ayub and Saad {Ali Alahmari} and Nadeem Qazi and Julie Wall},
keywords = {Reinforcement learning, Markov Decision Process, Bi-clustering, Q-learning, Policy},
abstract = {Collaborative filtering (CF) recommender systems are static in nature and does not adapt well with changing user preferences. User preferences may change after interaction with a system or after buying a product. Conventional CF clustering algorithms only identifies the distribution of patterns and hidden correlations globally. However, the impossibility of discovering local patterns by these algorithms, headed to the popularization of bi-clustering algorithms. Bi-clustering algorithms can analyze all dataset dimensions simultaneously and consequently, discover local patterns that deliver a better understanding of the underlying hidden correlations. In this paper, we modelled the recommendation problem as a sequential decision-making problem using Markov Decision Processes (MDP). To perform state representation for MDP, we first converted user-item votings matrix to a binary matrix. Then we performed bi-clustering on this binary matrix to determine a subset of similar rows and columns. A bi-cluster merging algorithm is designed to merge similar and overlapping bi-clusters. These bi-clusters are then mapped to a squared grid (SG). RL is applied on this SG to determine best policy to give recommendation to users. Start state is determined using Improved Triangle Similarity (ITR similarity measure. Reward function is computed as grid state overlapping in terms of users and items in current and prospective next state. A thorough comparative analysis was conducted, encompassing a diverse array of methodologies, including RL-based, pure Collaborative Filtering (CF), and clustering methods. The results demonstrate that our proposed method outperforms its competitors in terms of precision, recall, and optimal policy learning.}
}

@article{Soto2,
author={Soto-Forero, Daniel and Ackermann, Simha and Betbeder, Marie-Laure and Henriet, Julien},
title={Automatic Real-Time Adaptation of Training Session Difficulty Using Rules and Reinforcement Learning in the AI-VT ITS},
journal = {International Journal of Modern Education and Computer Science(IJMECS)},
volume = {16},
pages = {56-71},
year = {2024},
issn = {2075-0161},
doi = { https://doi.org/10.5815/ijmecs.2024.03.05},
url = {https://www.mecs-press.org/ijmecs/ijmecs-v16-n3/v16n3-5.html},
keywords={Real Time Adaptation, Intelligent Training System, Thompson Sampling, Case-Based Reasoning, Automatic Adaptation},
abstract={Some of the most common and typical issues in the field of intelligent tutoring systems (ITS) are (i) the correct identification of learners’ difficulties in the learning process, (ii) the adaptation of content or presentation of the system according to the difficulties encountered, and (iii) the ability to adapt without initial data (cold-start). In some cases, the system tolerates modifications after the realization and assessment of competences. Other systems require complicated real-time adaptation since only a limited number of data can be captured. In that case, it must be analyzed properly and with a certain precision in order to obtain the appropriate adaptations. Generally, for the adaptation step, the ITS gathers common learners together and adapts their training similarly. Another type of adaptation is more personalized, but requires acquired or estimated information about each learner (previous grades, probability of success, etc.). Some of these parameters may be difficult to obtain, and others are imprecise and can lead to misleading adaptations. The adaptation using machine learning requires prior training with a lot of data. This article presents a model for the real time automatic adaptation of a predetermined session inside an ITS called AI-VT. This adaptation process is part of a case-based reasoning global model. The characteristics of the model proposed in this paper (i) require a limited number of data in order to generate a personalized adaptation, (ii) do not require training, (iii) are based on the correlation to complexity levels, and (iv) are able to adapt even at the cold-start stage. The proposed model is presented with two different configurations, deterministic and stochastic. The model has been tested with a database of 1000 learners, corresponding to different knowledge levels in three different scenarios. The results show the dynamic adaptation of the proposed model in both versions, with the adaptations obtained helping the system to evolve more rapidly and identify learner weaknesses in the different levels of complexity as well as the generation of pertinent recommendations in specific cases for each learner capacity.}
}

@InProceedings{10.1007/978-3-031-63646-2_11 ,
author={Soto-Forero, Daniel and Betbeder, Marie-Laure and Henriet, Julien},
editor={Recio-Garcia, Juan A. and Orozco-del-Castillo, Mauricio G. and Bridge, Derek},
title={Ensemble Stacking Case-Based Reasoning for Regression},
booktitle={Case-Based Reasoning Research and Development},
year={2024},
publisher={Springer Nature Switzerland},
address={Cham},
pages={159--174},
abstract={This paper presents a case-based reasoning algorithm with a two-stage iterative double stacking to find approximate solutions to one and multidimensional regression problems. This approach does not require training, so it can work with dynamic data at run time. The solutions are generated using stochastic algorithms in order to allow exploration of the solution space. The evaluation is performed by transforming the regression problem into an optimization problem with an associated objective function. The algorithm has been tested in comparison with nine classical regression algorithms on ten different regression databases extracted from the UCI site. The results show that the proposed algorithm generates solutions in most cases quite close to the real solutions. According to the RMSE, the proposed algorithm globally among the four best algorithms, according to MAE, to the fourth best algorithms of the ten evaluated, suggesting that the results are reasonably good.},
isbn={978-3-031-63646-2}
}

@article{ZHANG2018189,
title = {A three learning states Bayesian knowledge tracing model},
journal = {Knowledge-Based Systems},
volume = {148},
pages = {189-201},
year = {2018},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2018.03.001},
url = {https://www.sciencedirect.com/science/article/pii/S0950705118301199},
author = {Kai Zhang and Yiyu Yao},
keywords = {Bayesian knowledge tracing, Three-way decisions},
abstract = {This paper proposes a Bayesian knowledge tracing model with three learning states by extending the original two learning states. We divide a learning process into three sections by using an evaluation function for three-way decisions. Advantages of such a trisection over traditional bisection are demonstrated by comparative experiments. We develop a three learning states model based on the trisection of the learning process. We apply the model to a series of comparative experiments with the original model. Qualitative and quantitative analyses of the experimental results indicate the superior performance of the proposed model over the original model in terms of prediction accuracies and related statistical measures.}
}

@article{Li_2024,
doi = {10.3847/1538-4357/ad3215},
url = {https://dx.doi.org/10.3847/1538-4357/ad3215},
year = {2024},
month = {apr},
publisher = {The American Astronomical Society},
volume = {965},
number = {2},
pages = {125},
author = {Zhigang Li and Zhejie Ding and Yu Yu and Pengjie Zhang},
title = {The Kullback–Leibler Divergence and the Convergence Rate of Fast Covariance Matrix Estimators in Galaxy Clustering Analysis},
journal = {The Astrophysical Journal},
abstract = {We present a method to quantify the convergence rate of the fast estimators of the covariance matrices in the large-scale structure analysis. Our method is based on the Kullback–Leibler (KL) divergence, which describes the relative entropy of two probability distributions. As a case study, we analyze the delete-d jackknife estimator for the covariance matrix of the galaxy correlation function. We introduce the information factor or the normalized KL divergence with the help of a set of baseline covariance matrices to diagnose the information contained in the jackknife covariance matrix. Using a set of quick particle mesh mock catalogs designed for the Baryon Oscillation Spectroscopic Survey DR11 CMASS galaxy survey, we find that the jackknife resampling method succeeds in recovering the covariance matrix with 10 times fewer simulation mocks than that of the baseline method at small scales (s ≤ 40 h −1 Mpc). However, the ability to reduce the number of mock catalogs is degraded at larger scales due to the increasing bias on the jackknife covariance matrix. Note that the analysis in this paper can be applied to any fast estimator of the covariance matrix for galaxy clustering measurements.}
}

﻿@Article{Kim2024,
author={Kim, Wonjik},
title={A Random Focusing Method with Jensen--Shannon Divergence for Improving Deep Neural Network Performance Ensuring Architecture Consistency},
journal={Neural Processing Letters},
year={2024},
month={Jun},
day={17},
volume={56},
number={4},
pages={199},
abstract={Multiple hidden layers in deep neural networks perform non-linear transformations, enabling the extraction of meaningful features and the identification of relationships between input and output data. However, the gap between the training and real-world data can result in network overfitting, prompting the exploration of various preventive methods. The regularization technique called 'dropout' is widely used for deep learning models to improve the training of robust and generalized features. During the training phase with dropout, neurons in a particular layer are randomly selected to be ignored for each input. This random exclusion of neurons encourages the network to depend on different subsets of neurons at different times, fostering robustness and reducing sensitivity to specific neurons. This study introduces a novel approach called random focusing, departing from complete neuron exclusion in dropout. The proposed random focusing selectively highlights random neurons during training, aiming for a smoother transition between training and inference phases while keeping network architecture consistent. This study also incorporates Jensen--Shannon Divergence to enhance the stability and efficacy of the random focusing method. Experimental validation across tasks like image classification and semantic segmentation demonstrates the adaptability of the proposed methods across different network architectures, including convolutional neural networks and transformers.},
issn={1573-773X},
doi={10.1007/s11063-024-11668-z},
url={https://doi.org/10.1007/s11063-024-11668-z}
}

@InProceedings{pmlr-v238-ou24a,
  title = 	 {Thompson Sampling Itself is Differentially Private},
  author =       {Ou, Tingting and Cummings, Rachel and Avella Medina, Marco},
  booktitle = 	 {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {1576--1584},
  year = 	 {2024},
  editor = 	 {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
  volume = 	 {238},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {02--04 May},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v238/ou24a/ou24a.pdf},
  url = 	 {https://proceedings.mlr.press/v238/ou24a.html},
  abstract = 	 {In this work we first show that the classical Thompson sampling algorithm for multi-arm bandits is differentially private as-is, without any modification. We provide per-round privacy guarantees as a function of problem parameters and show composition over $T$ rounds; since the algorithm is unchanged, existing $O(\sqrt{NT\log N})$ regret bounds still hold and there is no loss in performance due to privacy. We then show that simple modifications – such as pre-pulling all arms a fixed number of times, increasing the sampling variance – can provide tighter privacy guarantees. We again provide privacy guarantees that now depend on the new parameters introduced in the modification, which allows the analyst to tune the privacy guarantee as desired. We also provide a novel regret analysis for this new algorithm, and show how the new parameters also impact expected regret. Finally, we empirically validate and illustrate our theoretical findings in two parameter regimes and demonstrate that tuning the new parameters substantially improve the privacy-regret tradeoff.}
}

@Article{math12111758,
AUTHOR = {Uguina, Antonio R. and Gomez, Juan F. and Panadero, Javier and Martínez-Gavara, Anna and Juan, Angel A.},
TITLE = {A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem},
JOURNAL = {Mathematics},
VOLUME = {12},
YEAR = {2024},
NUMBER = {11},
ARTICLE-NUMBER = {1758},
URL = {https://www.mdpi.com/2227-7390/12/11/1758},
ISSN = {2227-7390},
ABSTRACT = {The team orienteering problem (TOP) is a well-studied optimization challenge in the field of Operations Research, where multiple vehicles aim to maximize the total collected rewards within a given time limit by visiting a subset of nodes in a network. With the goal of including dynamic and uncertain conditions inherent in real-world transportation scenarios, we introduce a novel dynamic variant of the TOP that considers real-time changes in environmental conditions affecting reward acquisition at each node. Specifically, we model the dynamic nature of environmental factors—such as traffic congestion, weather conditions, and battery level of each vehicle—to reflect their impact on the probability of obtaining the reward when visiting each type of node in a heterogeneous network. To address this problem, a learnheuristic optimization framework is proposed. It combines a metaheuristic algorithm with Thompson sampling to make informed decisions in dynamic environments. Furthermore, we conduct empirical experiments to assess the impact of varying reward probabilities on resource allocation and route planning within the context of this dynamic TOP, where nodes might offer a different reward behavior depending upon the environmental conditions. Our numerical results indicate that the proposed learnheuristic algorithm outperforms static approaches, achieving up to 25% better performance in highly dynamic scenarios. Our findings highlight the effectiveness of our approach in adapting to dynamic conditions and optimizing decision-making processes in transportation systems.},
DOI = {10.3390/math12111758}
}

@inproceedings{NEURIPS2023_9d8cf124,
 author = {Abel, David and Barreto, Andre and Van Roy, Benjamin and Precup, Doina and van Hasselt, Hado P and Singh, Satinder},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {A. Oh and T. Naumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},
 pages = {50377--50407},
 publisher = {Curran Associates, Inc.},
 title = {A Definition of Continual Reinforcement Learning},
 url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/9d8cf1247786d6dfeefeeb53b8b5f6d7-Paper-Conference.pdf},
 volume = {36},
 year = {2023}
}

@article{NGUYEN2024111566,
title = {Dynamic metaheuristic selection via Thompson Sampling for online optimization},
journal = {Applied Soft Computing},
volume = {158},
pages = {111566},
year = {2024},
issn = {1568-4946},
doi = {https://doi.org/10.1016/j.asoc.2024.111566},
url = {https://www.sciencedirect.com/science/article/pii/S1568494624003405},
author = {Alain Nguyen},
keywords = {Selection hyper-heuristic, Multi-armed-bandit, Thompson Sampling, Online optimization},
abstract = {It is acknowledged that no single heuristic can outperform all the others in every optimization problem. This has given rise to hyper-heuristic methods for providing solutions to a wider range of problems. In this work, a set of five non-competing low-level heuristics is proposed in a hyper-heuristic framework. The multi-armed bandit problem analogy is efficiently leveraged and Thompson Sampling is used to actively select the best heuristic for online optimization. The proposed method is compared against ten population-based metaheuristic algorithms on the well-known CEC’05 optimizing benchmark consisting of 23 functions of various landscapes. The results show that the proposed algorithm is the only one able to find the global minimum of all functions with remarkable consistency.}
}

﻿@Article{Malladi2024,
author={Malladi, Rama K.},
title={Application of Supervised Machine Learning Techniques to Forecast the COVID-19 U.S. Recession and Stock Market Crash},
journal={Computational Economics},
year={2024},
month={Mar},
day={01},
volume={63},
number={3},
pages={1021-1045},
abstract={Machine learning (ML), a transformational technology, has been successfully applied to forecasting events down the road. This paper demonstrates that supervised ML techniques can be used in recession and stock market crash (more than 20{\%} drawdown) forecasting. After learning from strictly past monthly data, ML algorithms detected the Covid-19 recession by December 2019, six months before the official NBER announcement. Moreover, ML algorithms foresaw the March 2020 S{\&}P500 crash two months before it happened. The current labor market and housing are harbingers of a future U.S. recession (in 3 months). Financial factors have a bigger role to play in stock market crashes than economic factors. The labor market appears as a top-two feature in predicting both recessions and crashes. ML algorithms detect that the U.S. exited recession before December 2020, even though the official NBER announcement has not yet been made. They also do not anticipate a U.S. stock market crash before March 2021. ML methods have three times higher false discovery rates of recessions compared to crashes.},
issn={1572-9974},
doi={10.1007/s10614-022-10333-8},
url={https://doi.org/10.1007/s10614-022-10333-8}
}

@INPROCEEDINGS{10493943,
  author={Raaa Subha and Naaa Gayathri and Saaa Sasireka and Raaa Sathiyabanu and Baaa Santhiyaa and Baaa Varshini},
  booktitle={2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)}, 
  title={Intelligent Tutoring Systems using Long Short-Term Memory Networks and Bayesian Knowledge Tracing}, 
  year={2024},
  volume={0},
  number={0},
  pages={24-29},
  keywords={Knowledge engineering;Filtering;Estimation;Transforms;Real-time systems;Bayes methods;Problem-solving;Intelligent Tutoring System (ITS);Long Short-Term Memory (LSTM);Bayesian Knowledge Tracing (BKT);Reinforcement Learning},
  doi={10.1109/ICMCSI61536.2024.00010}
}

@article{https//doi.org/10.1155/2024/4067721,
author = {Ahmed, Esmael},
title = {Student Performance Prediction Using Machine Learning Algorithms},
journal = {Applied Computational Intelligence and Soft Computing},
volume = {2024},
number = {1},
pages = {4067721},
doi = {https://doi.org/10.1155/2024/4067721},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1155/2024/4067721},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1155/2024/4067721},
abstract = {Education is crucial for a productive life and providing necessary resources. With the advent of technology like artificial intelligence, higher education institutions are incorporating technology into traditional teaching methods. Predicting academic success has gained interest in education as a strong academic record improves a university’s ranking and increases student employment opportunities. Modern learning institutions face challenges in analyzing performance, providing high-quality education, formulating strategies for evaluating students’ performance, and identifying future needs. E-learning is a rapidly growing and advanced form of education, where students enroll in online courses. Platforms like Intelligent Tutoring Systems (ITS), learning management systems (LMS), and massive open online courses (MOOC) use educational data mining (EDM) to develop automatic grading systems, recommenders, and adaptative systems. However, e-learning is still considered a challenging learning environment due to the lack of direct interaction between students and course instructors. Machine learning (ML) is used in developing adaptive intelligent systems that can perform complex tasks beyond human abilities. Some areas of applications of ML algorithms include cluster analysis, pattern recognition, image processing, natural language processing, and medical diagnostics. In this research work, K-means, a clustering data mining technique using Davies’ Bouldin method, obtains clusters to find important features affecting students’ performance. The study found that the SVM algorithm had the best prediction results after parameter adjustment, with a 96\% accuracy rate. In this paper, the researchers have examined the functions of the Support Vector Machine, Decision Tree, naive Bayes, and KNN classifiers. The outcomes of parameter adjustment greatly increased the accuracy of the four prediction models. Naïve Bayes model’s prediction accuracy is the lowest when compared to other prediction methods, as it assumes a strong independent relationship between features.},
year = {2024}
}

@article{HAZEM,
author = {Hazem A. Alrakhawi and Nurullizam Jamiat and Samy S. Abu-Naser},
title = {Intelligent Tutoring Systems in education: A systematic review of usage, tools, effects and evaluation},
journal = {Journal of Theoretical and Applied Information Technology},
volume = {2023},
number = {4},
pages = {4067721},
doi = {},
url = {},
abstract = {},
year = {2023}
}

﻿@Article{Liu2023,
author={Liu, Mengchi
and Yu, Dongmei},
title={Towards intelligent E-learning systems},
journal={Education and Information Technologies},
year={2023},
month={Jul},
day={01},
volume={28},
number={7},
pages={7845-7876},
abstract={The prevalence of e-learning systems has made educational resources more accessible, interactive and effective to learners without the geographic and temporal boundaries. However, as the number of users increases and the volume of data grows, current e-learning systems face some technical and pedagogical challenges. This paper provides a comprehensive review on the efforts of applying new information and communication technologies to improve e-learning services. We first systematically investigate current e-learning systems in terms of their classification, architecture, functions, challenges, and current trends. We then present a general architecture for big data based e-learning systems to meet the ever-growing demand for e-learning. We also describe how to use data generated in big data based e-learning systems to support more flexible and customized course delivery and personalized learning.},
issn={1573-7608},
doi={10.1007/s10639-022-11479-6},
url={https://doi.org/10.1007/s10639-022-11479-6}
}

@InProceedings{10.1007/978-3-031-63646-2_13,
author="Soto-Forero, Daniel
and Ackermann, Simha
and Betbeder, Marie-Laure
and Henriet, Julien",
editor="Recio-Garcia, Juan A.
and Orozco-del-Castillo, Mauricio G.
and Bridge, Derek",
title="The Intelligent Tutoring System AI-VT with Case-Based Reasoning and Real Time Recommender Models",
booktitle="Case-Based Reasoning Research and Development",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="191--205",
abstract="This paper presents a recommendation model coupled on an existing CBR system model through a new modular architecture designed to integrate multiple services in a learning system called AI-VT (Artificial Intelligence Training System). The recommendation model provides a semi-automatic review of the CBR, two variants of the recommendation model have been implemented: deterministic and stochastic. The model has been tested with 1000 simulated learners, and compared with an original CBR system and BKT (Bayesian Knowledge Tracing) recommender system. The results show that the proposed model identifies learners' weaknesses correctly and revises the content of the ITS (Intelligent Tutoring System) better than the original ITS with CBR. Compared to BKT, the results at each level of complexity are variable, but overall the proposed stochastic model obtains better results.",
isbn="978-3-031-63646-2"
}

@article{doi10.1137/23M1592420,
author = {Minsker, Stanislav and Strawn, Nate},
title = {The Geometric Median and Applications to Robust Mean Estimation},
journal = {SIAM Journal on Mathematics of Data Science},
volume = {6},
number = {2},
pages = {504-533},
year = {2024},
doi = {10.1137/23M1592420},
URL = { https://doi.org/10.1137/23M1592420},
eprint = {https://doi.org/10.1137/23M1592420},
abstract = { Abstract.This paper is devoted to the statistical and numerical properties of the geometric median and its applications to the problem of robust mean estimation via the median of means principle. Our main theoretical results include (a) an upper bound for the distance between the mean and the median for general absolutely continuous distributions in \(\mathbb R^d\), and examples of specific classes of distributions for which these bounds do not depend on the ambient dimension \(d\); (b) exponential deviation inequalities for the distance between the sample and the population versions of the geometric median, which again depend only on the trace-type quantities and not on the ambient dimension. As a corollary, we deduce improved bounds for the (geometric) median of means estimator that hold for large classes of heavy-tailed distributions. Finally, we address the error of numerical approximation, which is an important practical aspect of any statistical estimation procedure. We demonstrate that the objective function minimized by the geometric median satisfies a “local quadratic growth” condition that allows one to translate suboptimality bounds for the objective function to the corresponding bounds for the numerical approximation to the median itself and propose a simple stopping rule applicable to any optimization method which yields explicit error guarantees. We conclude with the numerical experiments, including the application to estimation of mean values of log-returns for S\&P 500 data. }
}

@article{lei2024analysis,
  title={Analysis of Simpson’s Paradox and Its Applications},
  author={Lei, Zhihao},
  journal={Highlights in Science, Engineering and Technology},
  volume={88},
  pages={357--362},
  year={2024}
}

@InProceedings{pmlr-v108-seznec20a,
  title = 	 {A single algorithm for both restless and rested rotting bandits},
  author =       {Seznec, Julien and Menard, Pierre and Lazaric, Alessandro and Valko, Michal},
  booktitle = 	 {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics},
  pages = 	 {3784--3794},
  year = 	 {2020},
  editor = 	 {Chiappa, Silvia and Calandra, Roberto},
  volume = 	 {108},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {26--28 Aug},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v108/seznec20a/seznec20a.pdf},
  url = 	 {https://proceedings.mlr.press/v108/seznec20a.html},
  abstract = 	 {In many application domains (e.g., recommender systems, intelligent tutoring systems), the rewards associated to the available actions tend to decrease over time. This decay is either caused by the actions executed in the past (e.g., a user may get bored when songs of the same genre are recommended over and over) or by an external factor (e.g., content becomes outdated). These two situations can be modeled as specific instances of the rested and restless bandit settings, where arms are rotting (i.e., their value decrease over time). These problems were thought to be significantly different, since Levine et al. (2017) showed that state-of-the-art algorithms for restless bandit perform poorly in the rested rotting setting. In this paper, we introduce a novel algorithm, Rotting Adaptive Window UCB (RAW-UCB), that achieves near-optimal regret in both rotting rested and restless bandit, without any prior knowledge of the setting (rested or restless) and the type of non-stationarity (e.g., piece-wise constant, bounded variation). This is in striking contrast with previous negative results showing that no algorithm can achieve similar results as soon as rewards are allowed to increase. We confirm our theoretical findings on a number of synthetic and dataset-based experiments.}
}

@article{doi10.3233/AIC-1994-7104,
author = {Agnar Aamodt and Enric Plaza},
title ={Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches},
journal = {AI Communications},
volume = {7},
number = {1},
pages = {39-59},
year = {1994},
doi = {10.3233/AIC-1994-7104},
URL = { 
https://journals.sagepub.com/doi/abs/10.3233/AIC-1994-7104
},
eprint = { 
https://journals.sagepub.com/doi/pdf/10.3233/AIC-1994-7104
},
 abstract = { Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture. }
}

@Book{schank+abelson77,
author = {Roger C. Schank and Robert P. Abelson},
title = {Scripts, Plans, Goals and Understanding: an Inquiry into Human Knowledge Structures},
publisher = {L. Erlbaum},
year = {1977},
address = {Hillsdale, NJ},
keywords = {PAM, SAM, TALE-SPIN, causality, conceptual dependency, goals, plans, scripts, semantic primitive, text understanding}
}

@article{KOLODNER1983281,
title = {Reconstructive memory: A computer model},
journal = {Cognitive Science},
volume = {7},
number = {4},
pages = {281-328},
year = {1983},
issn = {0364-0213},
doi = {https://doi.org/10.1016/S0364-0213(83)80002-0},
url = {https://www.sciencedirect.com/science/article/pii/S0364021383800020},
author = {Janet L. Kolodner},
abstract = {This study presents a process model of very long-term episodic memory. The process presented is a reconstructive process. The process involves application of three kinds of reconstructive strategies—component-to-context instantiation strategies, component-instantiation strategies, and context-to-context instantiation strategies. The first is used to direct search to appropriate conceptual categories in memory. The other two are used to direct search within the chosen conceptual category. A fourth type of strategy, called executive search strategies, guide search for concepts related to the one targeted for retrieval. A conceptual memory organization implied by human reconstructive memory is presented along with examples which motivate it. A basic retrieval algorithm is presented for traversing that stucture. Retrieval strategies arise from failures in that algorithm. The memory organization and retrieval processes are implemented in a computer program called CYRUS which stores events in the lives of former Secretaries of State Cyrus Vance and Edmund Muskie and answers questions posed in English concerning that information. Examples which motivate the process model are drawn from protocols of human memory search. Examples of CYRUS'S behavior demonstrate the implemented process model. Conclusions are drawn concerning retrieval failures and the relationship of episodic and semantic memory.}
}

@Book{Riesbeck1989,
author = {Riesbeck C.K. and Schank R.C.},
year = {1989},
title = {Inside Case-Based Reasoning},
publisher = {Psychology Press},
url = {https://doi.org/10.4324/9780203781821}
}

@article{ALABDULRAHMAN2021114061,
title = {Catering for unique tastes: Targeting grey-sheep users recommender systems through one-class machine learning},
journal = {Expert Systems with Applications},
volume = {166},
pages = {114061},
year = {2021},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2020.114061},
url = {https://www.sciencedirect.com/science/article/pii/S0957417420308241},
author = {Rabaa Alabdulrahman and Herna Viktor},
keywords = {Recommender systems, Model-based systems, Machine learning, Grey-sheep, One-class classification},
abstract = {In recommendation systems, the grey-sheep problem refers to users with unique preferences and tastes that make it difficult to develop accurate profiles. That is, the similarity search approach typically followed during the recommendation process fails to yield good results. Most research does not focus on such users and thus fails to cater to more exotic tastes and emerging trends, leading to a subsequent loss in revenue and marketing opportunities. One suggested solution is to use one-class classification to generate a prediction list for these users, where decision boundaries are learned that distinguish between normal and grey-sheep users. In this paper, we present the grey-sheep one-class recommendation (GSOR) framework designed to create accurate prediction models while taking both regular and grey-sheep users into account. In addition, we introduce a novel grey-sheep movie recommendation benchmark to be used by current and future researchers. When evaluating our GSOR framework against this benchmark, our results indicate the value of combining cluster analysis, outlier detection, and one-class learning to generate relevant and timely recommendation lists from data sets that contain grey-sheep users. Specifically, by employing one-class decision tree algorithms, our GSOR framework was able to outperform traditional collaborative filtering-based recommendation systems in both accuracy and model construction time. Furthermore, we report that having grey-sheep users in the system often had a positive impact on the learning and recommendation processes.}
}

@article{HU2025127130,
title = {A social importance and category enhanced cold-start user recommendation system},
journal = {Expert Systems with Applications},
volume = {277},
pages = {127130},
year = {2025},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2025.127130},
url = {https://www.sciencedirect.com/science/article/pii/S0957417425007523},
author = {Bin Hu and Yinghong Ma and Zhiyuan Liu and Hong Wang},
keywords = {Social recommendation, Graph neural network, Cold-start users, Social importance, Category information},
abstract = {Social recommendation, which utilizes social relations to enhance recommender systems, has gained increasing attention with the rapid development of online social platforms. Although numerous studies have underscored the efficacy of integrating personal social information to bolster the performance of such systems, social recommendations still face several problems. Firstly, the cold-start problem for items persists in recommendation tasks leveraging social information. Secondly, the importance of users within social networks is often disregarded, leading to biases in recommendation tasks utilizing social information. Thirdly, the lack of utilization of item category information makes learning representations of items and users insufficient. Hence, this paper proposes a novel social recommendation model, Social Importance and Category Enhanced Cold-Start User Recommendation System (SICERec). At first, potential preference information for cold-start users is incorporated into similar user modules, extracting user preference information from historical interaction data between users and items. After that, the significance of users within social networks is considered by integrating their centrality attributes, thereby enriching the semantic representation of users. Finally, category information of user historical interaction items is incorporated into the modeling process to enrich the semantics of items. Extensive experimental results demonstrate the significant advantages of our SICERec method. Our model exhibits a minimum improvement of 15.1% in RMSE and at least 26.2% in MAE compared to state-of-the-art models when evaluated on two real datasets. Additionally, ablation experiments are conducted to validate each module’s effectiveness and provide further insights into how users’ social attributes and preferences influence their choices. We release our code at https://github.com/BinHu129/SICERec.}
}

@inproceedings{wolf2024keep,
title={Keep the faith: Faithful explanations in convolutional neural networks for case-based reasoning},
author={Wolf, Tom Nuno and Bongratz, Fabian and Rickmann, Anne-Marie and P{\"o}lsterl, Sebastian and Wachinger, Christian},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
pages={5921--5929},
year={2024}
}

@article{PAREJASLLANOVARCED2024111469,
title = {Case-based selection of explanation methods for neural network image classifiers},
journal = {Knowledge-Based Systems},
volume = {288},
pages = {111469},
year = {2024},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2024.111469},
url = {https://www.sciencedirect.com/science/article/pii/S0950705124001047},
author = {Humberto Parejas-Llanovarced and Marta Caro-Martínez and Mauricio G. Orozco-del-Castillo and Juan A. Recio-García},
keywords = {Case-based reasoning, Explainable artificial intelligence, Explanation methods, Explanation of image classification},
abstract = {Deep learning is especially remarkable in terms of image classification. However, the outcomes of models are not explainable to users due to their complex nature, having an impact on the users’ trust in the provided classifications. To solve this problem, several explanation techniques have been proposed, but they greatly depend on the nature of the images being classified and the users’ perception of the explanations. In this work, we present Case-Based Reasoning as a learning-based solution to the problem of selecting the best explanation method for the image classifications obtained by models. We propose the elicitation of a case base that reflects the human perception of the quality of the explanations and how to reuse this knowledge to select the best explanation approach for a given image classification.}
}

@Article{buildings13030651,
AUTHOR = {Uysal, Furkan and Sonmez, Rifat},
TITLE = {Bootstrap Aggregated Case-Based Reasoning Method for Conceptual Cost Estimation},
JOURNAL = {Buildings},
VOLUME = {13},
YEAR = {2023},
NUMBER = {3},
ARTICLE-NUMBER = {651},
URL = {https://www.mdpi.com/2075-5309/13/3/651},
ISSN = {2075-5309},
ABSTRACT = {Conceptual cost estimation is an important step in project feasibility decisions when there is not enough information on detailed design and project requirements. Methods that enable quick and reasonably accurate conceptual cost estimates are crucial for achieving successful decisions in the early stages of construction projects. For this reason, numerous machine learning methods proposed in the literature that use different learning mechanisms. In recent years, the case-based reasoning (CBR) method has received particular attention in the literature for conceptual cost estimation of construction projects that use similarity-based learning principles. Despite the fact that CBR provides a powerful and practical alternative for conceptual cost estimation, one of the main criticisms about CBR is its low prediction performance when there is not a sufficient number of cases. This paper presents a bootstrap aggregated CBR method for achieving advancement in CBR research, particularly for conceptual cost estimation of construction projects when a limited number of training cases are available. The proposed learning method is designed so that CBR can learn from a diverse set of training data even when there are not a sufficient number of cases. The performance of the proposed bootstrap aggregated CBR method is evaluated using three data sets. The results revealed that the prediction performance of the new bootstrap aggregated CBR method is better than the prediction performance of the existing CBR method. Since the majority of conceptual cost estimates are made with a limited number of cases, the proposed method provides a contribution to CBR research and practice by improving the existing methods for conceptual cost estimating.},
DOI = {10.3390/buildings13030651}
}

@article{YU2023110163,
title = {A case-based reasoning driven ensemble learning paradigm for financial distress prediction with missing data},
journal = {Applied Soft Computing},
volume = {137},
pages = {110163},
year = {2023},
issn = {1568-4946},
doi = {https://doi.org/10.1016/j.asoc.2023.110163},
url = {https://www.sciencedirect.com/science/article/pii/S1568494623001813},
author = {Lean Yu and Mengxin Li},
keywords = {Case-based reasoning, Missing data imputation, Financial distress prediction, Imputation method, Ensemble learning},
abstract = {Financial distress prediction is often accompanied by missing sample data. For this purpose, a novel case-based reasoning (CBR) driven ensemble learning paradigm is proposed for financial distress prediction with missing data. In the proposed paradigm, three main stages, CBR-driven missing data imputation, CBR-driven single classifiers prediction, and CBR-driven ensemble result output, are involved. In the first stage, the CBR-driven missing data imputation method is used to fill in missing values in the initial dataset. Second, three different CBR-driven single classification models are constructed using Manhattan distance, Euclidean distance, and cosine distance to predict financial distress, respectively. In the final stage, the weighted majority voting strategy is used to ensemble prediction results of the CBR-driven single classification models to improve prediction accuracy and robustness. For illustration and verification, the experiments on datasets with different missing rates of six Chinese listed companies are performed. And corresponding results show that the proposed CBR-driven ensemble learning paradigm can effectively improve the imputation performance and increase the robustness of classification performance, indicating that the proposed CBR-driven ensemble learning paradigm can be used as a competitive solution to financial distress prediction with missing data.}
}

@InProceedings{10.1007/978-3-031-63646-2_4,
author="Malburg, Lukas
and Hotz, Maxim
and Bergmann, Ralph",
editor="Recio-Garcia, Juan A.
and Orozco-del-Castillo, Mauricio G.
and Bridge, Derek",
title="Improving Complex Adaptations in Process-Oriented Case-Based Reasoning by Applying Rule-Based Adaptation",
booktitle="Case-Based Reasoning Research and Development",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="50--66",
abstract="Adaptation is a complex and error-prone task in Case-Based Reasoning (CBR), including the adaptation knowledge acquisition and modeling efforts required for performing adaptations. This is also evident for the subfield of Process-Oriented Case-Based Reasoning (POCBR) in which cases represent procedural experiential knowledge, making creation and maintaining adaptation knowledge even for domain experts exceedingly challenging. Current adaptation methods in POCBR address the adaptation knowledge bottleneck by learning adaptation knowledge based on cases in the case base. However, these approaches are based on proprietary representation formats, resulting in low usability and maintainability. Therefore, we present an approach of using adaptation rules and rule engines for complex adaptations in POCBR in this paper. The results of an experimental evaluation indicate that the rule-based adaptation approach leads to significantly better results during runtime than an already available POCBR adaptation method.",
isbn="978-3-031-63646-2"
}

@article{YU2024123745,
title = {A two-stage case-based reasoning driven classification paradigm for financial distress prediction with missing and imbalanced data},
journal = {Expert Systems with Applications},
volume = {249},
pages = {123745},
year = {2024},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2024.123745},
url = {https://www.sciencedirect.com/science/article/pii/S0957417424006110},
author = {Lean Yu and Mengxin Li and Xiaojun Liu},
keywords = {Case-based reasoning, Missing data imputation, Imbalanced data classification, Financial distress prediction, Learning vector quantization clustering},
abstract = {Financial distress prediction often accompanies missing sample feature data and imbalanced normal and abnormal samples. To solve missing and imbalanced data that have significant negative impacts on the financial distress prediction model, a two-stage case-based reasoning (CBR)-driven classification paradigm is proposed to accurately and robustly predict financial distress. The proposed classification paradigm involves two main stages: CBR-driven missing data imputation and learning vector quantization (LVQ)-CBR-driven classifier prediction. In the first stage, the hybrid CBR-driven weighted imputation method is used to fill in missing values in the analytical dataset to obtain reliable and stable imputation performance, thereby solving the data missing problem. In the second stage, the LVQ-CBR-driven classification model is constructed to predict financial distress. By highlighting and fully learning minority abnormal samples, the classification model solves the low prediction accuracy of minority abnormal samples arising from data imbalance. For illustration and verification, some experiments are performed on seven Chinese-listed enterprise datasets with different missing and imbalance rates. Corresponding results show that the proposed two-stage CBR-driven classification paradigm can achieve the best imputation performance, greatly improve the prediction accuracy of minority abnormal samples, and integrally realize the best overall prediction performance compared with other imputation methods, imbalanced data processing methods, and their combinations. This implies that the proposed two-stage CBR-driven classification paradigm can be used as a competitive solution to financial distress prediction with missing and imbalanced data.}
}

@article{Sadeghi,
author = {Sadeghi Moghadam, Mohammad Reza and Jafarnejad, Ahmad and Heidary Dahooie, Jalil and Ghasemian Sahebi, Iman},
title = {A Hidden Markov Model Based Extended Case-Based Reasoning Algorithm for Relief Materials Demand Forecasting},
journal = {Mathematics Interdisciplinary Research},
volume = {9},
number = {1},
pages = {89-109},
year  = {2024},
publisher = {University of Kashan},
issn = {2538-3639}, 
eissn = {2476-4965}, 
doi = {10.22052/mir.2023.253148.1420},
abstract = {‎In emergency situations‎, ‎accurate demand forecasting for relief materials such as food‎, ‎water‎, ‎and medicine is crucial for effective disaster response‎. ‎This research is presented a novel algorithm to demand forecasting for relief materials using extended Case-Based Reasoning (CBR) with the best-worst method (BWM) and Hidden Markov Models (HMMs)‎. ‎The proposed algorithm involves training an HMM on historical data to obtain a set of state sequences representing the temporal fluctuations in demand for different relief materials‎. ‎When a new disaster occurs‎, ‎the algorithm first determines the current state sequence using the available data and searches the case library for past disasters with similar state sequences‎. ‎The effectiveness of the proposed algorithm is demonstrated through experiments on real-world disaster data of Iran‎. ‎Based on the results‎, ‎the forecasting error index for four relief materials is less than 10\%; therefore‎, ‎the proposed CBR-BWM-HMM is a strong and robust algorithm‎. },
keywords = {Demand forecasting‎,‎Emergency relief material‎,‎Case-based reasoning‎,‎Hidden Markov model‎},	
url = {https://mir.kashanu.ac.ir/article_114202.html},
eprint = {https://mir.kashanu.ac.ir/article_114202_5cd337237a7190d9fc380710f1680cbb.pdf}
}

@article{skittou2024recommender,
  title={A Recommender System for Educational Planning},
  author={Skittou, Mustapha and Merrouchi, Mohamed and Gadi, Taoufiq},
  journal={Cybernetics and Information Technologies},
  volume={24},
  number={2},
  pages={67--85},
  year={2024},
  publisher={Sciendo}
}

@Article{Kuzilek2017,
author={Kuzilek, Jakub
and Hlosta, Martin
and Zdrahal, Zdenek},
title={Open University Learning Analytics dataset},
journal={Scientific Data},
year={2017},
month={Nov},
day={28},
volume={4},
number={1},
pages={170171},
abstract={Learning Analytics focuses on the collection and analysis of learners' data to improve their learning experience by providing informed guidance and to optimise learning materials. To support the research in this area we have developed a dataset, containing data from courses presented at the Open University (OU). What makes the dataset unique is the fact that it contains demographic data together with aggregated clickstream data of students' interactions in the Virtual Learning Environment (VLE). This enables the analysis of student behaviour, represented by their actions. The dataset contains the information about 22 courses, 32,593 students, their assessment results, and logs of their interactions with the VLE represented by daily summaries of student clicks (10,655,280 entries). The dataset is freely available at https://analyse.kmi.open.ac.uk/open{\_}datasetunder a CC-BY 4.0 license.},
issn={2052-4463},
doi={10.1038/sdata.2017.171},
url={https://doi.org/10.1038/sdata.2017.171}
}

@misc{UCI,
  title = {Markelle Kelly, Rachel Longjohn, Kolby Nottingham, The UCI Machine Learning Repository},
  howpublished = {\url{https://archive.ics.uci.edu}},
  year={2024},
  note = {Accessed: 2024-09-30}
}

@misc{Data,
  title = {Jeux de données},
  howpublished = {\url{https://disc.univ-fcomte.fr/gitlab/daniel.soto_forero/ai-vt-recommender-system}},
  year={2023},
  note = {Accessed: 2023-11-20}
}

@article{CHEN2025104070,
title = {Incorporating Forgetting Curve and Memory Replay for Evolving Socially-aware Recommendation},
journal = {Information Processing and Management},
volume = {62},
number = {3},
pages = {104070},
year = {2025},
issn = {0306-4573},
doi = {https://doi.org/10.1016/j.ipm.2025.104070},
url = {https://www.sciencedirect.com/science/article/pii/S0306457325000123},
author = {Hongqi Chen and Zhiyong Feng and Shizhan Chen and Hongyue Wu and Yingchao Sun and Jingyu Li and Qinghang Gao and Lu Zhang and Xiao Xue},
keywords = {Social recommendation, Forgetting curve, Memory replay, Social consistency, Graph neural networks},
abstract = {Social recommendations play a crucial role in helping users filter information and discover potential requirements. However, existing works often ignore the effects of memory patterns and social inconsistency, which hide the recommendation for capturing evolving user interests. To overcome these problems, a model incorporating the Forgetting curve and Memory Replay for Evolving Socially-aware recommendation (FMRES) is proposed to navigate users’ fresh interests. Specifically, a cognitive-inspired Ebbinghaus curve is integrated with item attributes to consider users’ personalized interest forgetting and retention. Then, the memory replay mechanism is employed to revive forgotten yet valuable items, fostering user engagement and enhancing relevance in recommendations. By aggregating the neighbors’ social characters, consistent friends are sampled to identify meaningful and impactful relationships. Finally, temporal representations of users and items are incorporated to track the evolution of users’ interests by utilizing gated recurrent units. Extensive experiments on three datasets demonstrate that the proposed model consistently outperforms advanced baseline methods over various metrics.}
}

@book{busch2023teaching,
  title={Teaching and Learning Illuminated: The Big Ideas, Illustrated},
  author={Busch, B. and Watson, E. and Bogatchek, L.},
  isbn={9781000877960},
  lccn={2022056096},
  url={https://books.google.fr/books?id=ik-3EAAAQBAJ},
  year={2023},
  publisher={Taylor and Francis}
}

@InProceedings{soto4,
author={Soto-Forero, Daniel and Betbeder, Marie-Laure and Henriet, Julien},
editor={ Elise Lavoué, Margarida Romero, Yvan Peter},
title={Modèle de recommandation stochastique et de raisonnement à partir de cas appliqué à l'EIAH AI-VT},
booktitle={Actes de la 12ème Conférence sur les Environnements Informatiques pour l'Apprentissage Humain},
year={2025},
pages={588--594},
isbn={978-2-901384-10-6}
}

@InProceedings{soto5,
author={Soto-Forero, Daniel and Betbeder, Marie-Laure and Henriet, Julien},
editor={Antonios Gasteratos, Sabine Graf, Angelos Markos},
title={Ensemble Stacking Case-Based Reasoning and a Stochastic Recommender Algorithm with the Hawkes Process Applied to ITS AI-VT},
booktitle={International Conference on Intelligent Tutoring Systems},
year={2025},
isbn={0}
}

@article{soto6,
title = {Integration of Stacking Case-Based Reasoning with a Multi-Agent System Applied to the Intelligent Tutoring System AI-VT},
journal = {Journal},
volume = {0},
number = {0},
year = {2025},
issn = {0000-0000},
author={Soto-Forero, Daniel and Betbeder, Marie-Laure and Henriet, Julien},
}
