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@article{ZHANG2021100025,	1	File was deleted
title = {AI technologies for education: Recent research and future directions},	2
journal = {Computers and Education: Artificial Intelligence},	3
volume = {2},	4
pages = {100025},	5
language = {English},	6
year = {2021},	7
issn = {2666-920X},	8
type = {article},	9
doi = {https://doi.org/10.1016/j.caeai.2021.100025},	10
url = {https://www.sciencedirect.com/science/article/pii/S2666920X21000199},	11
author = {Ke Zhang. and Ayse Begum Aslan},	12
address={USA},	13
affiliation={Wayne State University; Eastern Michigan University},	14
keywords = {Artificial intelligence, AI, AI in Education},	15
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.}	16
}	17
	18
@article{PETROVIC201617,	19
title = {Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning},	20
journal = {Artificial Intelligence in Medicine},	21
volume = {68},	22
pages = {17-28},	23
year = {2016},	24
language = {English},	25
issn = {0933-3657},	26
type = {article},	27
doi = {https://doi.org/10.1016/j.artmed.2016.01.006},	28
url = {https://www.sciencedirect.com/science/article/pii/S093336571630015X},	29
author = {Sanja Petrovic and Gulmira Khussainova and Rupa Jagannathan},	30
affiliation={Nottingham University},	31
address={UK},	32
keywords = {Case-based reasoning, Adaptation-guided retrieval, Machine-learning tools, Radiotherapy treatment planning},	33
abstract = {Objective	34
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.	35
Methodology	36
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.	37
Results	38
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.	39
Conclusions	40
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.}	41
}	42
	43
@article{ROLDANREYES20151,	44
title = {Improvement of online adaptation knowledge acquisition and reuse in case-based reasoning: Application to process engineering design},	45
journal = {Engineering Applications of Artificial Intelligence},	46
volume = {41},	47
pages = {1-16},	48
affiliation={Université de Toulouse; Instituto Tecnologico de Orizaba},	49
country={France},	50
language = {English},	51
year = {2015},	52
type = {article},	53
issn = {0952-1976},	54
doi = {https://doi.org/10.1016/j.engappai.2015.01.015},	55
url = {https://www.sciencedirect.com/science/article/pii/S0952197615000263},	56
author = {E. {Roldan Reyes} and S. Negny and G. {Cortes Robles} and J.M. {Le Lann}},	57
keywords = {Case based reasoning, Constraint satisfaction problems, Interactive adaptation method, Online knowledge acquisition, Failure diagnosis and repair},	58
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.}	59
}	60
	61
@article{JUNG20095695,	62
title = {Integrating radial basis function networks with case-based reasoning for product design},	63
journal = {Expert Systems with Applications},	64
volume = {36},	65
number = {3, Part 1},	66
language = {English},	67
pages = {5695-5701},	68
year = {2009},	69
type = {article},	70
issn = {0957-4174},	71
doi = {https://doi.org/10.1016/j.eswa.2008.06.099},	72
url = {https://www.sciencedirect.com/science/article/pii/S0957417408003667},	73
author = {Sabum Jung and Taesoo Lim and Dongsoo Kim},	74
affiliation={LG Production Engineering Research Institute; Sungkyul University; Soongsil University},	75
keywords = {Case-based reasoning (CBR), Radial basis function network (RBFN), Design expert system, Product design},	76
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.}	77
}	78
	79
@article{CHIU2023100118,	80
title = {Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education},	81
journal = {Computers and Education: Artificial Intelligence},	82
volume = {4},	83
language = {English},	84
type = {article},	85
pages = {100118},	86
year = {2023},	87
issn = {2666-920X},	88
doi = {https://doi.org/10.1016/j.caeai.2022.100118},	89
url = {https://www.sciencedirect.com/science/article/pii/S2666920X2200073X},	90
author = {Thomas K.F. Chiu and Qi Xia and Xinyan Zhou and Ching Sing Chai and Miaoting Cheng},	91
keywords = {Artificial intelligence, Artificial intelligence in education, Systematic review, Learning, Teaching, Assessment},	92
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.}	93
}	94
	95
@article{Robertson2014ARO,	96
title = {A Review of Real-Time Strategy Game AI},	97
author = {Glen Robertson and Ian D. Watson},	98
affiliation = {University of Auckland },	99
keywords = {Game, IA, Real-time strategy},	100
type={article},	101
language={English},	102
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.},	103
journal = {AI Mag.},	104
year = {2014},	105
volume = {35},	106
pages = {75-104}	107
}	108
	109
@Inproceedings{10.1007/978-3-642-15973-2_50,	110
author={Butdee, S.	111
and Tichkiewitch, S.},	112
affiliation={University of Technology North Bangkok; Grenoble Institute of Technology},	113
editor={Bernard, Alain},	114
title={Case-Based Reasoning for Adaptive Aluminum Extrusion Die Design Together with Parameters by Neural Networks},	115
keywords={Adaptive die design and parameters, Optimal aluminum extrusion, Case-based reasoning, Neural networks},	116
booktitle={Global Product Development},	117
year={2011},	118
type = {article; proceedings paper},	119
language = {English},	120
publisher = {Springer Berlin Heidelberg},	121
address = {Berlin, Heidelberg},	122
pages = {491--496},	123
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.},	124
isbn = {978-3-642-15973-2}	125
}	126
	127
@Inproceedings{10.1007/978-3-319-47096-2_11,	128
author={Grace, Kazjon	129
and Maher, Mary Lou	130
and Wilson, David C.	131
and Najjar, Nadia A.},	132
affiliation={University of North Carolina at Charlotte},	133
editor={Goel, Ashok	134
and D{\'i}az-Agudo, M Bel{\'e}n	135
and Roth-Berghofer, Thomas},	136
title={Combining CBR and Deep Learning to Generate Surprising Recipe Designs},	137
keywords={Case-based reasoning, deep learning, recipe design},	138
type = {article; proceedings paper},	139
booktitle={Case-Based Reasoning Research and Development},	140
year={2016},	141
publisher={Springer International Publishing},	142
address={Cham},	143
language = {English},	144
pages={154--169},	145
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.},	146
isbn={978-3-319-47096-2}	147
}	148
	149
@Inproceedings{10.1007/978-3-319-61030-6_1,	150
author={Maher, Mary Lou	151
and Grace, Kazjon},	152
editor={Aha, David W.	153
and Lieber, Jean},	154
affiliation={University of North Carolina at Charlotte},	155
title={Encouraging Curiosity in Case-Based Reasoning and Recommender Systems},	156
keywords={Curiosity, Case-based reasoning, Recommender systems},	157
booktitle={Case-Based Reasoning Research and Development},	158
year={2017},	159
publisher={Springer International Publishing},	160
address={Cham},	161
pages={3--15},	162
language = {English},	163
type = {article; proceedings paper},	164
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.},	165
isbn={978-3-319-61030-6}	166
}	167
	168
@Inproceedings{Muller,	169
author = {Müller, G. and Bergmann, R.},	170
affiliation={University of Trier},	171
year = {2015},	172
month = {01},	173
language = {English},	174
type = {article; proceedings paper},	175
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.},	176
booktitle = {International Conference on Case-Based Reasoning},	177
title = {CookingCAKE: A Framework for the adaptation of cooking recipes represented as workflows},	178
keywords={recipe adaptation, workflow adaptation, workflows, process-oriented, case based reasoning}	179
}	180
	181
@Inproceedings{10.1007/978-3-319-24586-7_20,	182
author={Onta{\~{n}}{\'o}n, S.	183
and Plaza, E.	184
and Zhu, J.},	185
editor={H{\"u}llermeier, Eyke	186
and Minor, Mirjam},	187
affiliation={Drexel University; Artificial Intelligence Research Institute CSIC},	188
title={Argument-Based Case Revision in CBR for Story Generation},	189
keywords={CBR, Case-based reasoning, Story generation},	190
booktitle={Case-Based Reasoning Research and Development},	191
year={2015},	192
publisher={Springer International Publishing},	193
address={Cham},	194
language = {English},	195
pages={290--305},	196
type = {article; proceedings paper},	197
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.},	198
isbn={978-3-319-24586-7}	199
}	200
	201
@Inproceedings{10.1007/978-3-030-58342-2_20,	202
author={Lepage, Yves	203
and Lieber, Jean	204
and Mornard, Isabelle	205
and Nauer, Emmanuel	206
and Romary, Julien	207
and Sies, Reynault},	208
editor={Watson, Ian	209
and Weber, Rosina},	210
title={The French Correction: When Retrieval Is Harder to Specify than Adaptation},	211
affiliation={Waseda University; Université de Lorraine},	212
keywords={case-based reasoning, retrieval, analogy, sentence correction},	213
booktitle={Case-Based Reasoning Research and Development},	214
year={2020},	215
language = {English},	216
type = {article; proceedings paper},	217
publisher={Springer International Publishing},	218
address={Cham},	219
pages={309--324},	220
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.},	221
isbn={978-3-030-58342-2}	222
}	223
	224
@Inproceedings{10.1007/978-3-030-01081-2_25,	225
author={Smyth, Barry	226
and Cunningham, P{\'a}draig},	227
editor={Cox, Michael T.	228
and Funk, Peter	229
and Begum, Shahina},	230
affiliation={University College Dublin},	231
title={An Analysis of Case Representations for Marathon Race Prediction and Planning},	232
keywords={Marathon planning, Case representation, Case-based reasoning},	233
booktitle={Case-Based Reasoning Research and Development},	234
year={2018},	235
language = {English},	236
publisher={Springer International Publishing},	237
address={Cham},	238
pages={369--384},	239
type = {article; proceedings paper},	240
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.},	241
isbn={978-3-030-01081-2}	242
}	243
	244
@Inproceedings{10.1007/978-3-030-58342-2_8,	245
author={Smyth, Barry	246
and Willemsen, Martijn C.},	247
editor={Watson, Ian	248
and Weber, Rosina},	249
affiliation={University College Dublin; Eindhoven University of Technology},	250
title={Predicting the Personal-Best Times of Speed Skaters Using Case-Based Reasoning},	251
keywords={CBR for health and exercise, speed skating, race-time prediction, case representation},	252
booktitle={Case-Based Reasoning Research and Development},	253
year={2020},	254
type = {article; proceedings paper},	255
language = {English},	256
publisher={Springer International Publishing},	257
address={Cham},	258
pages={112--126},	259
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.},	260
isbn={978-3-030-58342-2}	261
}	262
	263
@Inproceedings{10.1007/978-3-030-58342-2_5,	264
author={Feely, Ciara	265
and Caulfield, Brian	266
and Lawlor, Aonghus	267
and Smyth, Barry},	268
editor={Watson, Ian	269
and Weber, Rosina},	270
affiliation={University College Dublin},	271
title={Using Case-Based Reasoning to Predict Marathon Performance and Recommend Tailored Training Plans},	272
keywords={CBR for health and exercise, marathon running, race-time prediction, plan recommendation},	273
booktitle={Case-Based Reasoning Research and Development},	274
year={2020},	275
language = {English},	276
publisher={Springer International Publishing},	277
address={Cham},	278
pages={67--81},	279
type = {article; proceedings paper},	280
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.},	281
isbn={978-3-030-58342-2}	282
}	283
	284
@article{LALITHA2020583,	285
title = {Personalised Self-Directed Learning Recommendation System},	286
journal = {Procedia Computer Science},	287
volume = {171},	288
pages = {583-592},	289
year = {2020},	290
type = {article},	291
language = {English},	292
note = {Third International Conference on Computing and Network Communications (CoCoNet'19)},	293
issn = {1877-0509},	294
doi = {https://doi.org/10.1016/j.procs.2020.04.063},	295
url = {https://www.sciencedirect.com/science/article/pii/S1877050920310309},	296
author = {T B Lalitha and P S Sreeja},	297
affiliation={Hindustan Institute of Technology and Science},	298
keywords = {e-Learning, PSDLR, Recommendation System, SDL, Self-Directed Learning},	299
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.}	300
}	301
	302
@article{Zhou2021,	303
author={Zhou, Lina	304
and Wang, Chunxia},	305
affiliation={Baotou Medical College},	306
title={Research on Recommendation of Personalized Exercises in English Learning Based on Data Mining},	307
journal={Scientific Programming},	308
year={2021},	309
month={Dec},	310
type = {article},	311
language = {English},	312
day={21},	313
publisher={Hindawi},	314
keywords={Recommender systems, Learning},	315
volume={2021},	316
pages={5042286},	317
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.},	318
issn={1058-9244},	319
doi={10.1155/2021/5042286},	320
url={https://doi.org/10.1155/2021/5042286}	321
}	322
	323
@article{INGKAVARA2022100086,	324
title = {The use of a personalized learning approach to implementing self-regulated online learning},	325
journal = {Computers and Education: Artificial Intelligence},	326
volume = {3},	327
pages = {100086},	328
type = {article},	329
language = {English},	330
year = {2022},	331
issn = {2666-920X},	332
doi = {https://doi.org/10.1016/j.caeai.2022.100086},	333
url = {https://www.sciencedirect.com/science/article/pii/S2666920X22000418},	334
author = {Thanyaluck Ingkavara and Patcharin Panjaburee and Niwat Srisawasdi and Suthiporn Sajjapanroj},	335
keywords = {Intelligent tutoring system, Personalization, Adaptive learning, E-learning, TAM, Artificial intelligence},	336
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.}	337
}	338
	339
@article{HUANG2023104684,	340
title = {Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom},	341
journal = {Computers and Education},	342
volume = {194},	343
pages = {104684},	344
year = {2023},	345
language = {English},	346
type = {article},	347
issn = {0360-1315},	348
doi = {https://doi.org/10.1016/j.compedu.2022.104684},	349
url = {https://www.sciencedirect.com/science/article/pii/S036013152200255X},	350
author = {Anna Y.Q. Huang and Owen H.T. Lu and Stephen J.H. Yang},	351
keywords = {Data science applications in education, Distance education and online learning, Improving classroom teaching},	352
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.}	353
}	354
	355
@article{ZHAO2023118535,	356
title = {A recommendation system for effective learning strategies: An integrated approach using context-dependent DEA},	357
journal = {Expert Systems with Applications},	358
volume = {211},	359
pages = {118535},	360
year = {2023},	361
language = {English},	362
type = {article},	363
issn = {0957-4174},	364
doi = {https://doi.org/10.1016/j.eswa.2022.118535},	365
url = {https://www.sciencedirect.com/science/article/pii/S0957417422016104},	366
author = {Lu-Tao Zhao and Dai-Song Wang and Feng-Yun Liang and Jian Chen},	367
keywords = {Recommendation system, Learning strategies, Context-dependent DEA, Efficiency analysis},	368
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.}	369
}	370
	371
@article{SU2022109547,	372
title = {Graph-based cognitive diagnosis for intelligent tutoring systems},	373
journal = {Knowledge-Based Systems},	374
volume = {253},	375
pages = {109547},	376
year = {2022},	377
language = {English},	378
type = {article},	379
issn = {0950-7051},	380
doi = {https://doi.org/10.1016/j.knosys.2022.109547},	381
url = {https://www.sciencedirect.com/science/article/pii/S095070512200778X},	382
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},	383
keywords = {Cognitive diagnosis, Graph neural networks, Interpretable machine learning},	384
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.}	385
}	386
	387
@article{EZALDEEN2022100700,	388
title = {A hybrid E-learning recommendation integrating adaptive profiling and sentiment analysis},	389
journal = {Journal of Web Semantics},	390
volume = {72},	391
pages = {100700},	392
year = {2022},	393
type = {article},	394
language = {English},	395
issn = {1570-8268},	396
doi = {https://doi.org/10.1016/j.websem.2021.100700},	397
url = {https://www.sciencedirect.com/science/article/pii/S1570826821000664},	398
author = {Hadi Ezaldeen and Rachita Misra and Sukant Kishoro Bisoy and Rawaa Alatrash and Rojalina Priyadarshini},	399
keywords = {Hybrid E-learning recommendation, Adaptive profiling, Semantic learner profile, Fine-grained sentiment analysis, Convolutional Neural Network, Word embeddings},	400
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.}	401
}	402
	403
@article{MUANGPRATHUB2020e05227,	404
title = {Learning recommendation with formal concept analysis for intelligent tutoring system},	405
journal = {Heliyon},	406
volume = {6},	407
number = {10},	408
pages = {e05227},	409
language = {English},	410
type = {article},	411
year = {2020},	412
issn = {2405-8440},	413
doi = {https://doi.org/10.1016/j.heliyon.2020.e05227},	414
url = {https://www.sciencedirect.com/science/article/pii/S2405844020320703},	415
author = {Jirapond Muangprathub and Veera Boonjing and Kosin Chamnongthai},	416
keywords = {Computer Science, Learning recommendation, Formal concept analysis, Intelligent tutoring system, Adaptive learning},	417
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.}	418
}	419
	420
@article{min8100434,	421
author = {Leikola, Maria and Sauer, Christian and Rintala, Lotta and Aromaa, Jari and Lundström, Mari},	422
title = {Assessing the Similarity of Cyanide-Free Gold Leaching Processes: A Case-Based Reasoning Application},	423
journal = {Minerals},	424
volume = {8},	425
type = {article},	426
language = {English},	427
year = {2018},	428
number = {10},	429
url = {https://www.mdpi.com/2075-163X/8/10/434},	430
issn = {2075-163X},	431
keywords={hydrometallurgy, cyanide-free gold, knowledge modelling, case-based reasoning, information retrieval},	432
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’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’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.},	433
doi = {10.3390/min8100434}	434
}	435
	436
@article{10.1145/3459665,	437
author = {Cunningham, P\'{a}draig and Delany, Sarah Jane},	438
title = {K-Nearest Neighbour Classifiers - A Tutorial},	439
year = {2021},	440
issue_date = {July 2022},	441
publisher = {Association for Computing Machinery},	442
address = {New York, NY, USA},	443
type={article},	444
language={English},	445
volume = {54},	446
number = {6},	447
issn = {0360-0300},	448
url = {https://doi.org/10.1145/3459665},	449
doi = {10.1145/3459665},	450
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.},	451
journal = {ACM Comput. Surv.},	452
month = {jul},	453
articleno = {128},	454
numpages = {25},	455
keywords = {k-Nearest neighbour classifiers}	456
}	457
	458
@article{9072123,	459
author={Sinaga, Kristina P. and Yang, Miin-Shen},	460
journal={IEEE Access},	461
type={article},	462
language={English},	463
title={Unsupervised K-Means Clustering Algorithm},	464
year={2020},	465
volume={8},	466
number={},	467
pages={80716-80727},	468
doi={10.1109/ACCESS.2020.2988796}	469
}	470
	471
@article{WANG2021331,	472
title = {A new prediction strategy for dynamic multi-objective optimization using Gaussian Mixture Model},	473
journal = {Information Sciences},	474
volume = {580},	475
type = {article},	476
language = {English},	477
pages = {331-351},	478
year = {2021},	479
issn = {0020-0255},	480
doi = {https://doi.org/10.1016/j.ins.2021.08.065},	481
url = {https://www.sciencedirect.com/science/article/pii/S0020025521008732},	482
author = {Feng Wang and Fanshu Liao and Yixuan Li and Hui Wang},	483
keywords = {Dynamic multi-objective optimization, Gaussian Mixture Model, Change type detection, Resampling},	484
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.}	485
}	486
	487
@article{9627973,	488
author={Xu, Shengbing and Cai, Wei and Xia, Hongxi and Liu, Bo and Xu, Jie},	489
journal={IEEE Access},	490
title={Dynamic Metric Accelerated Method for Fuzzy Clustering},	491
year={2021},	492
type={article},	493
language={English},	494
volume={9},	495
number={},	496
pages={166838-166854},	497
doi={10.1109/ACCESS.2021.3131368}	498
}	499
	500
@article{9434422,	501
author={Gupta, Samarth and Chaudhari, Shreyas and Joshi, Gauri and Yağan, Osman},	502
journal={IEEE Transactions on Information Theory},	503
title={Multi-Armed Bandits With Correlated Arms},	504
year={2021},	505
language={English},	506
type={article},	507
volume={67},	508
number={10},	509
pages={6711-6732},	510
doi={10.1109/TIT.2021.3081508}	511
}	512
	513
@Inproceedings{8495930,	514
author={Supic, H.},	515
booktitle={2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)},	516
title={Case-Based Reasoning Model for Personalized Learning Path Recommendation in Example-Based Learning Activities},	517
year={2018},	518
type={article},	519
language={English},	520
volume={},	521
number={},	522
pages={175-178},	523
doi={10.1109/WETICE.2018.00040}	524
}	525
	526
@Inproceedings{9870279,	527
author={Lin, Baihan},	528
booktitle={2022 IEEE Congress on Evolutionary Computation (CEC)},	529
title={Evolutionary Multi-Armed Bandits with Genetic Thompson Sampling},	530
year={2022},	531
type={article},	532
language={English},	533
volume={},	534
number={},	535
pages={1-8},	536
doi={10.1109/CEC55065.2022.9870279}	537
}	538
	539
@article{Obeid,	540
author={Obeid, C. and Lahoud, C. and Khoury, H. E. and Champin, P.},	541
title={A Novel Hybrid Recommender System Approach for Student Academic Advising Named COHRS, Supported by Case-based Reasoning and Ontology},	542
journal={Computer Science and Information Systems},	543
type={article},	544
language={English},	545
volume={19},	546
number={2},	547
pages={979–1005},	548
year={2022},	549
doi={https://doi.org/10.2298/CSIS220215011O}	550
}	551
	552
@book{Nkambou,	553
author = {Nkambou, R. and Bourdeau, J. and Mizoguchi, R.},	554
title = {Advances in Intelligent Tutoring Systems},	555
year = {2010},	556
type = {article},	557
language = {English},	558
publisher = {Springer Berlin, Heidelberg},	559
edition = {1}	560
}	561
	562
@book{hajduk2019cognitive,	563
title={Cognitive Multi-agent Systems: Structures, Strategies and Applications to Mobile Robotics and Robosoccer},	564
author={Hajduk, M. and Sukop, M. and Haun, M.},	565
type={book},	566
language={English},	567
isbn={9783319936857},	568
series={Studies in Systems, Decision and Control},	569
year={2019},	570
publisher={Springer International Publishing}	571
}	572
	573
@article{RICHTER20093,	574
title = {The search for knowledge, contexts, and Case-Based Reasoning},	575
journal = {Engineering Applications of Artificial Intelligence},	576
language = {English},	577
type = {article},	578
volume = {22},	579
number = {1},	580
pages = {3-9},	581
year = {2009},	582
issn = {0952-1976},	583
doi = {https://doi.org/10.1016/j.engappai.2008.04.021},	584
url = {https://www.sciencedirect.com/science/article/pii/S095219760800078X},	585
author = {Michael M. Richter},	586
keywords = {Case-Based Reasoning, Knowledge, Processes, Utility, Context},	587
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.}	588
}	589
	590
@Thesis{Marie,	591
author={Marie, F.},	592
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},	593
type={diplomathesis},	594
language={French},	595
institution={Université de Franche-Comte},	596
year={2019}	597
}	598
	599
@book{Hoang,	600
title = {La formule du savoir. Une philosophie unifiée du savoir fondée sur le théorème de Bayes},	601
author = {Hoang, L.N.},	602
type = {book},	603
language = {French},	604
isbn = {9782759822607},	605
year = {2018},	606
publisher = {EDP Sciences}	607
}	608
	609
@book{Richter2013,	610
title={Case-Based Reasoning (A Textbook)},	611
author={Richter, M. and Weber, R.},	612
type={book},	613
language={English},	614
isbn={9783642401664},	615
year={2013},	616
publisher={Springer-Verlag GmbH}	617
}	618
	619
@book{kedia2020hands,	620
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},	621
author={Kedia, A. and Rasu, M.},	622
language={English},	623
type={book},	624
isbn={9781838982584},	625
url={https://books.google.fr/books?id=1AbuDwAAQBAJ},	626
year={2020},	627
publisher={Packt Publishing}	628
}	629
	630
@book{ghosh2019natural,	631
title={Natural Language Processing Fundamentals: Build intelligent applications that can interpret the human language to deliver impactful results},	632
author={Ghosh, S. and Gunning, D.},	633
language={English},	634
type={book},	635
isbn={9781789955989},	636
url={https://books.google.fr/books?id=i8-PDwAAQBAJ},	637
year={2019},	638
publisher={Packt Publishing}	639
}	640
	641
@article{Akerblom,	642
title={Online learning of network bottlenecks via minimax paths},	643
author={kerblom, Niklas and Hoseini, Fazeleh Sadat and Haghir Chehreghani, Morteza},	644
language={English},	645
type={article},	646
volume = {122},	647
year = {2023},	648
issn = {1573-0565},	649
doi = {https://doi.org/10.1007/s10994-022-06270-0},	650
url = {https://doi.org/10.1007/s10994-022-06270-0},	651
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.}	652
}	653
	654
@article{Simen,	655
title={Dynamic slate recommendation with gated recurrent units and Thompson sampling},	656
author={Eide, Simen and Leslie, David S. and Frigessi, Arnoldo},	657
language={English},	658
type={article},	659
volume = {36},	660
year = {2022},	661
issn = {1573-756X},	662
doi = {https://doi.org/10.1007/s10618-022-00849-w},	663
url = {https://doi.org/10.1007/s10618-022-00849-w},	664
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.}	665
}	666
	667
@Inproceedings{Arthurs,	668
author={Arthurs, Noah and Stenhaug, Ben and Karayev, Sergey and Piech, Chris},	669
booktitle={International Conference on Educational Data Mining (EDM)},	670
title={Grades Are Not Normal: Improving Exam Score Models Using the Logit-Normal Distribution},	671
year={2019},	672
type={article},	673
language={English},	674
volume={},	675
number={},	676
pages={6},	677
url={https://eric.ed.gov/?id=ED599204}	678
}	679
	680
@article{Bahramian,	681
title={A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning},	682
author={Bahramian, Zahra and Ali Abbaspour, Rahim and Claramunt, Christophe},	683
language={English},	684
type={article},	685
year = {2017},	686
issn = {1574-017X},	687
doi = {https://doi.org/10.1155/2017/9364903},	688
url = {https://doi.org/10.1155/2017/9364903},	689
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.}	690
}	691
	692
@Thesis{Daubias2011,	693
author={Sthéphanie Jean-Daubias},	694
title={Ingénierie des profils d'apprenants},	695
type={diplomathesis},	696
language={French},	697
institution={Université Claude Bernard Lyon 1},	698
year={2011}	699
}	700
	701
@article{Tapalova,	702
author = {Olga Tapalova and Nadezhda Zhiyenbayeva},	703
title ={Artificial Intelligence in Education: AIEd for Personalised Learning Pathways},	704
journal = {Electronic Journal of e-Learning},	705
volume = {},	706
number = {},	707
pages = {15},	708
year = {2022},	709
URL = {https://eric.ed.gov/?q=Artificial+Intelligence+in+Education%3a+AIEd+for+Personalised+Learning+Pathways&id=EJ1373006},	710
language={English},	711
type={article},	712
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.}	713
}	714
	715
@article{Auer,	716
title = {From monolithic systems to Microservices: An assessment framework},	717
journal = {Information and Software Technology},	718
volume = {137},	719
pages = {106600},	720
year = {2021},	721
issn = {0950-5849},	722
doi = {https://doi.org/10.1016/j.infsof.2021.106600},	723
url = {https://www.sciencedirect.com/science/article/pii/S0950584921000793},	724
author = {Florian Auer and Valentina Lenarduzzi and Michael Felderer and Davide Taibi},	725
keywords = {Microservices, Cloud migration, Software measurement},	726
abstract = {Context:	727
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.	728
Objective:	729
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.	730
Method:	731
We conducted a survey done in the form of interviews with professionals to derive the assessment framework based on Grounded Theory.	732
Results:	733
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.}	734
}	735
	736
@Article{jmse10040464,	737
AUTHOR = {Zuluaga, Carlos A. and Aristizábal, Luis M. and Rúa, Santiago and Franco, Diego A. and Osorio, Dorie A. and Vásquez, Rafael E.},	738
TITLE = {Development of a Modular Software Architecture for Underwater Vehicles Using Systems Engineering},	739
JOURNAL = {Journal of Marine Science and Engineering},	740
VOLUME = {10},	741
YEAR = {2022},	742
NUMBER = {4},	743
ARTICLE-NUMBER = {464},	744
URL = {https://www.mdpi.com/2077-1312/10/4/464},	745
ISSN = {2077-1312},	746
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.},	747
DOI = {10.3390/jmse10040464}	748
}	749
	750
@article{doi:10.1177/1754337116651013,	751
author = {Julien Henriet and Lang Christophe and Philippe Laurent},	752
title ={Artificial Intelligence-Virtual Trainer: An educative system based on artificial intelligence and designed to produce varied and consistent training lessons},	753
journal = {Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology},	754
volume = {231},	755
number = {2},	756
pages = {110-124},	757
year = {2017},	758
doi = {10.1177/1754337116651013},	759
URL = {https://doi.org/10.1177/1754337116651013},	760
eprint = {https://doi.org/10.1177/1754337116651013},	761
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. }	762
}	763
	764
@InProceedings{10.1007/978-3-030-01081-2_9,	765
author="Henriet, Julien	766
and Greffier, Fran{\c{c}}oise",	767
editor="Cox, Michael T.	768
and Funk, Peter	769
and Begum, Shahina",	770
title="AI-VT: An Example of CBR that Generates a Variety of Solutions to the Same Problem",	771
booktitle="Case-Based Reasoning Research and Development",	772
year="2018",	773
publisher="Springer International Publishing",	774
address="Cham",	775
pages="124--139",	776
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.",	777
isbn="978-3-030-01081-2"	778
}	779
	780
@article{BAKUROV2021100913,	781
title = {Genetic programming for stacked generalization},	782
journal = {Swarm and Evolutionary Computation},	783
volume = {65},	784
pages = {100913},	785
year = {2021},	786
issn = {2210-6502},	787
doi = {https://doi.org/10.1016/j.swevo.2021.100913},	788
url = {https://www.sciencedirect.com/science/article/pii/S2210650221000742},	789
author = {Illya Bakurov and Mauro Castelli and Olivier Gau and Francesco Fontanella and Leonardo Vanneschi},	790
keywords = {Genetic Programming, Stacking, Ensemble Learning, Stacked Generalization},	791
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.}	792
}	793
	794
@article{Liang,	795
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},	796
Title={A Stacking Ensemble Learning Framework for Genomic Prediction},	797
Journal={Frontiers in Genetics},	798
year={2021},	799
doi ={10.3389/fgene.2021.600040},	800
PMID={33747037},	801
PMCID={PMC7969712}	802
}	803
	804
@Article{cmc.2023.033417,	805
AUTHOR = {Jeonghoon Choi and Dongjun Suh and Marc-Oliver Otto},	806
TITLE = {Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification},	807
JOURNAL = {Computers, Materials \& Continua},	808
VOLUME = {74},	809
YEAR = {2023},	810
NUMBER = {2},	811
PAGES = {2945--2966},	812
URL = {http://www.techscience.com/cmc/v74n2/50296},	813
ISSN = {1546-2226},	814
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.},	815
DOI = {10.32604/cmc.2023.033417}	816
}	817
	818
@ARTICLE{10.3389/fgene.2021.600040,	819
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},	820
TITLE={A Stacking Ensemble Learning Framework for Genomic Prediction},	821
JOURNAL={Frontiers in Genetics},	822
VOLUME={12},	823
YEAR={2021},	824
URL={https://www.frontiersin.org/articles/10.3389/fgene.2021.600040},	825
DOI={10.3389/fgene.2021.600040},	826
ISSN={1664-8021},	827
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.}	828
}	829
	830
@article{DIDDEN2023338,	831
title = {Decentralized learning multi-agent system for online machine shop scheduling problem},	832
journal = {Journal of Manufacturing Systems},	833
volume = {67},	834
pages = {338-360},	835
year = {2023},	836
issn = {0278-6125},	837
doi = {https://doi.org/10.1016/j.jmsy.2023.02.004},	838
url = {https://www.sciencedirect.com/science/article/pii/S0278612523000286},	839
author = {Jeroen B.H.C. Didden and Quang-Vinh Dang and Ivo J.B.F. Adan},	840
keywords = {Multi-agent system, Decentralized systems, Learning algorithm, Industry 4.0, Smart manufacturing},	841
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.}	842
}	843
	844
@article{REZAEI20221,	845
title = {A Biased Inferential Naivety learning model for a network of agents},	846
journal = {Cognitive Systems Research},	847
volume = {76},	848
pages = {1-12},	849
year = {2022},	850
issn = {1389-0417},	851
doi = {https://doi.org/10.1016/j.cogsys.2022.07.001},	852
url = {https://www.sciencedirect.com/science/article/pii/S1389041722000298},	853
author = {Zeinab Rezaei and Saeed Setayeshi and Ebrahim Mahdipour},	854
keywords = {Bayesian decision making, Heuristic method, Inferential naivety assumption, Observational learning, Social learning},	855
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.}	856
}	857
	858
@article{KAMALI2023110242,	859
title = {An immune inspired multi-agent system for dynamic multi-objective optimization},	860
journal = {Knowledge-Based Systems},	861
volume = {262},	862
pages = {110242},	863
year = {2023},	864
issn = {0950-7051},	865
doi = {https://doi.org/10.1016/j.knosys.2022.110242},	866
url = {https://www.sciencedirect.com/science/article/pii/S0950705122013387},	867
author = {Seyed Ruhollah Kamali and Touraj Banirostam and Homayun Motameni and Mohammad Teshnehlab},	868
keywords = {Immune inspired multi-agent system, Dynamic multi-objective optimization, Severe and frequent changes},	869
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.}	870
}	871
	872
@article{ZHANG2023110564,	873
title = {A novel human learning optimization algorithm with Bayesian inference learning},	874
journal = {Knowledge-Based Systems},	875
volume = {271},	876
pages = {110564},	877
year = {2023},	878
issn = {0950-7051},	879
doi = {https://doi.org/10.1016/j.knosys.2023.110564},	880
url = {https://www.sciencedirect.com/science/article/pii/S0950705123003143},	881
author = {Pinggai Zhang and Ling Wang and Zixiang Fei and Lisheng Wei and Minrui Fei and Muhammad Ilyas Menhas},	882
keywords = {Human learning optimization, Meta-heuristic, Bayesian inference, Bayesian inference learning, Individual learning, Social learning},	883
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.}	884
}	885
	886
@article{HIPOLITO2023103510,	887
title = {Breaking boundaries: The Bayesian Brain Hypothesis for perception and prediction},	888
journal = {Consciousness and Cognition},	889
volume = {111},	890
pages = {103510},	891
year = {2023},	892
issn = {1053-8100},	893
doi = {https://doi.org/10.1016/j.concog.2023.103510},	894
url = {https://www.sciencedirect.com/science/article/pii/S1053810023000478},	895
author = {Inês Hipólito and Michael Kirchhoff},	896
keywords = {Bayesian Brain Hypothesis, Modularity of the Mind, Cognitive processes, Informational boundaries},	897
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.}	898
}	899
	900
@article{LI2023424,	901
title = {Multi-agent evolution reinforcement learning method for machining parameters optimization based on bootstrap aggregating graph attention network simulated environment},	902
journal = {Journal of Manufacturing Systems},	903
volume = {67},	904
pages = {424-438},	905
year = {2023},	906
issn = {0278-6125},	907
doi = {https://doi.org/10.1016/j.jmsy.2023.02.015},	908
url = {https://www.sciencedirect.com/science/article/pii/S0278612523000390},	909
author = {Weiye Li and Songping He and Xinyong Mao and Bin Li and Chaochao Qiu and Jinwen Yu and Fangyu Peng and Xin Tan},	910
keywords = {Surface roughness, Cutting efficiency, Machining parameters optimization, Graph attention network, Multi-agent reinforcement learning, Evolutionary learning},	911
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.}	912
}	913
	914
@inproceedings{10.1145/3290605.3300912,	915
author = {Kim, Yea-Seul and Walls, Logan A. and Krafft, Peter and Hullman, Jessica},	916
title = {A Bayesian Cognition Approach to Improve Data Visualization},	917
year = {2019},	918
isbn = {9781450359702},	919
publisher = {Association for Computing Machinery},	920
address = {New York, NY, USA},	921
url = {https://doi.org/10.1145/3290605.3300912},	922
doi = {10.1145/3290605.3300912},	923
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.},	924
booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems},	925
pages = {1–14},	926
numpages = {14},	927
keywords = {bayesian cognition, uncertainty elicitation, visualization},	928
location = {Glasgow, Scotland Uk},	929
series = {CHI '19}	930
}	931
	932
@article{DYER2024104827,	933
title = {Black-box Bayesian inference for agent-based models},	934
journal = {Journal of Economic Dynamics and Control},	935
volume = {161},	936
pages = {104827},	937
year = {2024},	938
issn = {0165-1889},	939
doi = {https://doi.org/10.1016/j.jedc.2024.104827},	940
url = {https://www.sciencedirect.com/science/article/pii/S0165188924000198},	941
author = {Joel Dyer and Patrick Cannon and J. Doyne Farmer and Sebastian M. Schmon},	942
keywords = {Agent-based models, Bayesian inference, Neural networks, Parameter estimation, Simulation-based inference, Time series},	943
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.}	944
}	945
	946
@Article{Nikpour2021,	947
author={Nikpour, Hoda	948
and Aamodt, Agnar},	949
title={Inference and reasoning in a Bayesian knowledge-intensive CBR system},	950
journal={Progress in Artificial Intelligence},	951
year={2021},	952
month={Mar},	953
day={01},	954
volume={10},	955
number={1},	956
pages={49-63},	957
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.},	958
issn={2192-6360},	959
doi={10.1007/s13748-020-00223-1},	960
url={https://doi.org/10.1007/s13748-020-00223-1}	961
}	962
	963
@article{PRESCOTT2024112577,	964
title = {Efficient multifidelity likelihood-free Bayesian inference with adaptive computational resource allocation},	965
journal = {Journal of Computational Physics},	966
volume = {496},	967
pages = {112577},	968
year = {2024},	969
issn = {0021-9991},	970
doi = {https://doi.org/10.1016/j.jcp.2023.112577},	971
url = {https://www.sciencedirect.com/science/article/pii/S0021999123006721},	972
author = {Thomas P. Prescott and David J. Warne and Ruth E. Baker},	973
keywords = {Likelihood-free Bayesian inference, Multifidelity approaches},	974
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.}	975
}	976
	977
@article{RISTIC202030,	978
title = {A tutorial on uncertainty modeling for machine reasoning},	979
journal = {Information Fusion},	980
volume = {55},	981
pages = {30-44},	982
year = {2020},	983
issn = {1566-2535},	984
doi = {https://doi.org/10.1016/j.inffus.2019.08.001},	985
url = {https://www.sciencedirect.com/science/article/pii/S1566253519301976},	986
author = {Branko Ristic and Christopher Gilliam and Marion Byrne and Alessio Benavoli},	987
keywords = {Information fusion, Uncertainty, Imprecision, Model based classification, Bayesian, Random sets, Belief function theory, Possibility functions, Imprecise probability},	988
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.}	989
}	990
	991
@article{CICIRELLO2022108619,	992
title = {Machine learning based optimization for interval uncertainty propagation},	993
journal = {Mechanical Systems and Signal Processing},	994
volume = {170},	995
pages = {108619},	996
year = {2022},	997
issn = {0888-3270},	998
doi = {https://doi.org/10.1016/j.ymssp.2021.108619},	999
url = {https://www.sciencedirect.com/science/article/pii/S0888327021009493},	1000
author = {Alice Cicirello and Filippo Giunta},	1001
keywords = {Bounded uncertainty, Bayesian optimization, Expensive-to-evaluate deterministic computer models, Gaussian process, Communicating uncertainty},	1002
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.}	1003
}	1004
	1005
@INPROCEEDINGS{9278071,	1006
author={Petit, Maxime and Dellandrea, Emmanuel and Chen, Liming},	1007
booktitle={2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)},	1008
title={Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction},	1009
year={2020},	1010
volume={},	1011
number={},	1012
pages={1-8},	1013
keywords={Optimization;Robots;Task analysis;Bayes methods;Visualization;Service robots;Cognition;developmental robotics;long-term memory;meta learning;hyperparmeters automatic optimization;case-based reasoning},	1014
doi={10.1109/ICDL-EpiRob48136.2020.9278071}	1015
}	1016
	1017
@article{LI2023477,	1018
title = {Hierarchical and partitioned planning strategy for closed-loop devices in low-voltage distribution network based on improved KMeans partition method},	1019
journal = {Energy Reports},	1020
volume = {9},	1021
pages = {477-485},	1022
year = {2023},	1023
note = {2022 The 3rd International Conference on Power and Electrical Engineering},	1024
issn = {2352-4847},	1025
doi = {https://doi.org/10.1016/j.egyr.2023.05.161},	1026
url = {https://www.sciencedirect.com/science/article/pii/S2352484723009137},	1027
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},	1028
keywords = {Closed-loop device, Distribution network partition, Device planning, Hierarchical planning, Improved KMeans partition method},	1029
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.}	1030
}	1031
	1032
@article{SAXENA2024100838,	1033
title = {Hybrid KNN-SVM machine learning approach for solar power forecasting},	1034
journal = {Environmental Challenges},	1035
volume = {14},	1036
pages = {100838},	1037
year = {2024},	1038
issn = {2667-0100},	1039
doi = {https://doi.org/10.1016/j.envc.2024.100838},	1040
url = {https://www.sciencedirect.com/science/article/pii/S2667010024000040},	1041
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},	1042
keywords = {Solar power forecasting, Hybrid model, KNN, Optimization, Solar energy, SVM},	1043
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.}	1044
}	1045
	1046
@article{RAKESH2023100898,	1047
title = {Moving object detection using modified GMM based background subtraction},	1048
journal = {Measurement: Sensors},	1049
volume = {30},	1050
pages = {100898},	1051
year = {2023},	1052
issn = {2665-9174},	1053
doi = {https://doi.org/10.1016/j.measen.2023.100898},	1054
url = {https://www.sciencedirect.com/science/article/pii/S2665917423002349},	1055
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}},	1056
keywords = {Background subtraction, Gaussian mixture models, Intelligent video surveillance, Object detection},	1057
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.}	1058
}	1059
	1060
@article{JIAO2022540,	1061
title = {Interpretable fuzzy clustering using unsupervised fuzzy decision trees},	1062
journal = {Information Sciences},	1063
volume = {611},	1064
pages = {540-563},	1065
year = {2022},	1066
issn = {0020-0255},	1067
doi = {https://doi.org/10.1016/j.ins.2022.08.077},	1068
url = {https://www.sciencedirect.com/science/article/pii/S0020025522009872},	1069
author = {Lianmeng Jiao and Haoyu Yang and Zhun-ga Liu and Quan Pan},	1070
keywords = {Fuzzy clustering, Interpretable clustering, Unsupervised decision tree, Cluster merging},	1071
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.}	1072
}	1073
	1074
@article{ARNAUGONZALEZ2023101516,	1075
title = {A methodological approach to enable natural language interaction in an Intelligent Tutoring System},	1076
journal = {Computer Speech and Language},	1077
volume = {81},	1078
pages = {101516},	1079
year = {2023},	1080
issn = {0885-2308},	1081
doi = {https://doi.org/10.1016/j.csl.2023.101516},	1082
url = {https://www.sciencedirect.com/science/article/pii/S0885230823000359},	1083
author = {Pablo Arnau-González and Miguel Arevalillo-Herráez and Romina Albornoz-De Luise and David Arnau},	1084
keywords = {Intelligent tutoring systems (ITS), Interactive learning environments (ILE), Conversational agents, Rasa, Natural language understanding (NLU), Natural language processing (NLP)},	1085
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.}	1086
}	1087
	1088
@article{MAO20224065,	1089
title = {An Exploratory Approach to Intelligent Quiz Question Recommendation},	1090
journal = {Procedia Computer Science},	1091
volume = {207},	1092
pages = {4065-4074},	1093
year = {2022},	1094
note = {Knowledge-Based and Intelligent Information and Engineering Systems: Proceedings of the 26th International Conference KES2022},	1095
issn = {1877-0509},	1096
doi = {https://doi.org/10.1016/j.procs.2022.09.469},	1097
url = {https://www.sciencedirect.com/science/article/pii/S1877050922013631},	1098
author = {Kejie Mao and Qiwen Dong and Ye Wang and Daocheng Honga},	1099
keywords = {question recommendation, two-sided recommender systems, reinforcement learning, intelligent tutoring},	1100
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.}	1101
}	1102
	1103
@article{CLEMENTE2022118171,	1104
title = {A proposal for an adaptive Recommender System based on competences and ontologies},	1105
journal = {Expert Systems with Applications},	1106
volume = {208},	1107
pages = {118171},	1108
year = {2022},	1109
issn = {0957-4174},	1110
doi = {https://doi.org/10.1016/j.eswa.2022.118171},	1111
url = {https://www.sciencedirect.com/science/article/pii/S0957417422013392},	1112
author = {Julia Clemente and Héctor Yago and Javier {de Pedro-Carracedo} and Javier Bueno},	1113
keywords = {Recommender system, , Ontology network, Methodological development, Student modeling},	1114
abstract = {Context:	1115
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.	1116
Objective:	1117
To facilitate this goal, in this paper a new approach to develop an adaptive competence-based recommender system is proposed.	1118
Method:	1119
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.	1120
Results:	1121
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.	1122
Conclusions:	1123
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.}	1124
}	1125
	1126
@article{https://doi.org/10.1155/2023/2578286,	1127
author = {Li, Linqing and Wang, Zhifeng},	1128
title = {Knowledge Graph-Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness},	1129
journal = {International Journal of Intelligent Systems},	1130
volume = {2023},	1131
number = {1},	1132
pages = {2578286},	1133
doi = {https://doi.org/10.1155/2023/2578286},	1134
url = {https://onlinelibrary.wiley.com/doi/abs/10.1155/2023/2578286},	1135
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1155/2023/2578286},	1136
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.},	1137
year = {2023}	1138
}	1139
	1140
@inproceedings{badier:hal-04092828,	1141
TITLE = {{Comprendre les usages et effets d'un syst{\`e}me de recommandations p{\'e}dagogiques en contexte d'apprentissage non-formel}},	1142
AUTHOR = {Badier, Ana{\"e}lle and Lefort, Mathieu and Lefevre, Marie},	1143
URL = {https://hal.science/hal-04092828},	1144
BOOKTITLE = {{EIAH'23}},	1145
ADDRESS = {Brest, France},	1146
YEAR = {2023},	1147
MONTH = Jun,	1148
HAL_ID = {hal-04092828},	1149
HAL_VERSION = {v1},	1150
}	1151
	1152
@article{BADRA2023108920,	1153
title = {Case-based prediction – A survey},	1154
journal = {International Journal of Approximate Reasoning},	1155
volume = {158},	1156
pages = {108920},	1157
year = {2023},	1158
issn = {0888-613X},	1159
doi = {https://doi.org/10.1016/j.ijar.2023.108920},	1160
url = {https://www.sciencedirect.com/science/article/pii/S0888613X23000440},	1161
author = {Fadi Badra and Marie-Jeanne Lesot},	1162
keywords = {Case-based prediction, Analogical transfer, Similarity},	1163
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.}	1164
}	1165
	1166
	1167
@Article{jmse11050890 ,	1168
AUTHOR = {Louvros, Panagiotis and Stefanidis, Fotios and Boulougouris, Evangelos and Komianos, Alexandros and Vassalos, Dracos},	1169
TITLE = {Machine Learning and Case-Based Reasoning for Real-Time Onboard Prediction of the Survivability of Ships},	1170
JOURNAL = {Journal of Marine Science and Engineering},	1171
VOLUME = {11},	1172
YEAR = {2023},	1173
NUMBER = {5},	1174
ARTICLE-NUMBER = {890},	1175
URL = {https://www.mdpi.com/2077-1312/11/5/890},	1176
ISSN = {2077-1312},	1177
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.},	1178
DOI = {10.3390/jmse11050890}	1179
}	1180
	1181
	1182
@Article{su14031366,	1183
AUTHOR = {Chun, Se-Hak and Jang, Jae-Won},	1184
TITLE = {A New Trend Pattern-Matching Method of Interactive Case-Based Reasoning for Stock Price Predictions},	1185
JOURNAL = {Sustainability},	1186
VOLUME = {14},	1187
YEAR = {2022},	1188
NUMBER = {3},	1189
ARTICLE-NUMBER = {1366},	1190
URL = {https://www.mdpi.com/2071-1050/14/3/1366},	1191
ISSN = {2071-1050},	1192
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.},	1193
DOI = {10.3390/su14031366}	1194
}	1195
	1196
@Article{fire7040107,	1197
AUTHOR = {Pei, Qiuyan and Jia, Zhichao and Liu, Jia and Wang, Yi and Wang, Junhui and Zhang, Yanqi},	1198
TITLE = {Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning},	1199
JOURNAL = {Fire},	1200
VOLUME = {7},	1201
YEAR = {2024},	1202
NUMBER = {4},	1203
ARTICLE-NUMBER = {107},	1204
URL = {https://www.mdpi.com/2571-6255/7/4/107},	1205
ISSN = {2571-6255},	1206
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.},	1207
DOI = {10.3390/fire7040107}	1208
}	1209
	1210
@Article{Desmarais2012,	1211
author={Desmarais, Michel C.	1212
and Baker, Ryan S. J. d.},	1213
title={A review of recent advances in learner and skill modeling in intelligent learning environments},	1214
journal={User Modeling and User-Adapted Interaction},	1215
year={2012},	1216
month={Apr},	1217
day={01},	1218
volume={22},	1219
number={1},	1220
pages={9-38},	1221
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.},	1222
issn={1573-1391},	1223
doi={10.1007/s11257-011-9106-8},	1224
url={https://doi.org/10.1007/s11257-011-9106-8}	1225
}	1226
	1227
@article{Eide,	1228
title={Dynamic slate recommendation with gated recurrent units and Thompson sampling},	1229
author={Eide, Simen and Leslie, David S. and Frigessi, Arnoldo},	1230
language={English},	1231
type={article},	1232
volume = {36},	1233
year = {2022},	1234
issn = {1573-756X},	1235
doi = {https://doi.org/10.1007/s10618-022-00849-w},	1236
url = {https://doi.org/10.1007/s10618-022-00849-w},	1237
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.}	1238
}	1239
	1240
@InProceedings{10.1007/978-3-031-09680-8_14,	1241
author={Sablayrolles, Louis	1242
and Lefevre, Marie	1243
and Guin, Nathalie	1244
and Broisin, Julien},	1245
editor={Crossley, Scott	1246
and Popescu, Elvira},	1247
title={Design and Evaluation of a Competency-Based Recommendation Process},	1248
booktitle={Intelligent Tutoring Systems},	1249
year={2022},	1250
publisher={Springer International Publishing},	1251
address={Cham},	1252
pages={148--160},	1253
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.},	1254
isbn={978-3-031-09680-8}	1255
}	1256
	1257
@inproceedings{10.1145/3578337.3605122,	1258
author = {Xu, Shuyuan and Ge, Yingqiang and Li, Yunqi and Fu, Zuohui and Chen, Xu and Zhang, Yongfeng},	1259
title = {Causal Collaborative Filtering},	1260
year = {2023},	1261
isbn = {9798400700736},	1262
publisher = {Association for Computing Machinery},	1263
address = {New York, NY, USA},	1264
url = {https://doi.org/10.1145/3578337.3605122},	1265
doi = {10.1145/3578337.3605122},	1266
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.},	1267
booktitle = {Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval},	1268
pages = {235–245},	1269
numpages = {11},	1270
keywords = {recommender systems, counterfactual reasoning, collaborative filtering, causal analysis, Simpson's paradox},	1271
location = {Taipei, Taiwan},	1272
series = {ICTIR '23}	1273
}	1274
	1275
@inproceedings{10.1145/3583780.3615048,	1276
author = {Zhu, Zheqing and Van Roy, Benjamin},	1277
title = {Scalable Neural Contextual Bandit for Recommender Systems},	1278
year = {2023},	1279
isbn = {9798400701245},	1280
publisher = {Association for Computing Machinery},	1281
address = {New York, NY, USA},	1282
url = {https://doi.org/10.1145/3583780.3615048},	1283
doi = {10.1145/3583780.3615048},	1284
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.},	1285
booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},	1286
pages = {3636–3646},	1287
numpages = {11},	1288
keywords = {contextual bandits, decision making under uncertainty, exploration vs exploitation, recommender systems, reinforcement learning},	1289
location = {Birmingham, United Kingdom},	1290
series = {CIKM '23}	1291
}	1292
	1293
@ARTICLE{10494875,	1294
author={Ghoorchian, Saeed and Kortukov, Evgenii and Maghsudi, Setareh},	1295
journal={IEEE Open Journal of Signal Processing},	1296
title={Non-Stationary Linear Bandits With Dimensionality Reduction for Large-Scale Recommender Systems},	1297
year={2024},	1298
volume={5},	1299
number={},	1300
pages={548-558},	1301
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},	1302
doi={10.1109/OJSP.2024.3386490}	1303
}	1304
	1305
@article{GIANNIKIS2024111752,	1306
title = {Reinforcement learning for addressing the cold-user problem in recommender systems},	1307
journal = {Knowledge-Based Systems},	1308
volume = {294},	1309
pages = {111752},	1310
year = {2024},	1311
issn = {0950-7051},	1312
doi = {https://doi.org/10.1016/j.knosys.2024.111752},	1313
url = {https://www.sciencedirect.com/science/article/pii/S0950705124003873},	1314
author = {Stelios Giannikis and Flavius Frasincar and David Boekestijn},	1315
keywords = {Recommender systems, Reinforcement learning, Active learning, Cold-user problem},	1316
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.}	1317
}	1318
	1319
@article{IFTIKHAR2024121541,	1320
title = {A reinforcement learning recommender system using bi-clustering and Markov Decision Process},	1321
journal = {Expert Systems with Applications},	1322
volume = {237},	1323
pages = {121541},	1324
year = {2024},	1325
issn = {0957-4174},	1326
doi = {https://doi.org/10.1016/j.eswa.2023.121541},	1327
url = {https://www.sciencedirect.com/science/article/pii/S0957417423020432},	1328
author = {Arta Iftikhar and Mustansar Ali Ghazanfar and Mubbashir Ayub and Saad {Ali Alahmari} and Nadeem Qazi and Julie Wall},	1329
keywords = {Reinforcement learning, Markov Decision Process, Bi-clustering, Q-learning, Policy},	1330
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.}	1331
}	1332
	1333
@article{Soto2,	1334
author={Soto-Forero, Daniel and Ackermann, Simha and Betbeder, Marie-Laure and Henriet, Julien},	1335
title={Automatic Real-Time Adaptation of Training Session Difficulty Using Rules and Reinforcement Learning in the AI-VT ITS},	1336
journal = {International Journal of Modern Education and Computer Science(IJMECS)},	1337
volume = {16},	1338
pages = {56-71},	1339
year = {2024},	1340
issn = {2075-0161},	1341
doi = { https://doi.org/10.5815/ijmecs.2024.03.05},	1342
url = {https://www.mecs-press.org/ijmecs/ijmecs-v16-n3/v16n3-5.html},	1343
keywords={Real Time Adaptation, Intelligent Training System, Thompson Sampling, Case-Based Reasoning, Automatic Adaptation},	1344
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.}	1345
}	1346
	1347
@InProceedings{10.1007/978-3-031-63646-2_11 ,	1348
author={Soto-Forero, Daniel and Betbeder, Marie-Laure and Henriet, Julien},	1349
editor={Recio-Garcia, Juan A. and Orozco-del-Castillo, Mauricio G. and Bridge, Derek},	1350
title={Ensemble Stacking Case-Based Reasoning for Regression},	1351
booktitle={Case-Based Reasoning Research and Development},	1352
year={2024},	1353
publisher={Springer Nature Switzerland},	1354
address={Cham},	1355
pages={159--174},	1356
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.},	1357
isbn={978-3-031-63646-2}	1358
}	1359
	1360
@article{ZHANG2018189,	1361
title = {A three learning states Bayesian knowledge tracing model},	1362
journal = {Knowledge-Based Systems},	1363
volume = {148},	1364
pages = {189-201},	1365
year = {2018},	1366
issn = {0950-7051},	1367
doi = {https://doi.org/10.1016/j.knosys.2018.03.001},	1368
url = {https://www.sciencedirect.com/science/article/pii/S0950705118301199},	1369
author = {Kai Zhang and Yiyu Yao},	1370
keywords = {Bayesian knowledge tracing, Three-way decisions},	1371
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.}	1372
}	1373
	1374
@article{Li_2024,	1375
doi = {10.3847/1538-4357/ad3215},	1376
url = {https://dx.doi.org/10.3847/1538-4357/ad3215},	1377
year = {2024},	1378
month = {apr},	1379
publisher = {The American Astronomical Society},	1380
volume = {965},	1381
number = {2},	1382
pages = {125},	1383
author = {Zhigang Li and Zhejie Ding and Yu Yu and Pengjie Zhang},	1384
title = {The Kullback–Leibler Divergence and the Convergence Rate of Fast Covariance Matrix Estimators in Galaxy Clustering Analysis},	1385
journal = {The Astrophysical Journal},	1386
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.}	1387
}	1388
	1389
@Article{Kim2024,	1390
author={Kim, Wonjik},	1391
title={A Random Focusing Method with Jensen--Shannon Divergence for Improving Deep Neural Network Performance Ensuring Architecture Consistency},	1392
journal={Neural Processing Letters},	1393
year={2024},	1394
month={Jun},	1395
day={17},	1396
volume={56},	1397
number={4},	1398
pages={199},	1399
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.},	1400
issn={1573-773X},	1401
doi={10.1007/s11063-024-11668-z},	1402
url={https://doi.org/10.1007/s11063-024-11668-z}	1403
}	1404
	1405
@InProceedings{pmlr-v238-ou24a,	1406
title = {Thompson Sampling Itself is Differentially Private},	1407
author = {Ou, Tingting and Cummings, Rachel and Avella Medina, Marco},	1408
booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},	1409
pages = {1576--1584},	1410
year = {2024},	1411
editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},	1412
volume = {238},	1413
series = {Proceedings of Machine Learning Research},	1414
month = {02--04 May},	1415
publisher = {PMLR},	1416
pdf = {https://proceedings.mlr.press/v238/ou24a/ou24a.pdf},	1417
url = {https://proceedings.mlr.press/v238/ou24a.html},	1418
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.}	1419
}	1420
	1421
@Article{math12111758,	1422
AUTHOR = {Uguina, Antonio R. and Gomez, Juan F. and Panadero, Javier and Martínez-Gavara, Anna and Juan, Angel A.},	1423
TITLE = {A Learnheuristic Algorithm Based on Thompson Sampling for the Heterogeneous and Dynamic Team Orienteering Problem},	1424
JOURNAL = {Mathematics},	1425
VOLUME = {12},	1426
YEAR = {2024},	1427
NUMBER = {11},	1428
ARTICLE-NUMBER = {1758},	1429
URL = {https://www.mdpi.com/2227-7390/12/11/1758},	1430
ISSN = {2227-7390},	1431
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.},	1432
DOI = {10.3390/math12111758}	1433
}	1434
	1435
@inproceedings{NEURIPS2023_9d8cf124,	1436
author = {Abel, David and Barreto, Andre and Van Roy, Benjamin and Precup, Doina and van Hasselt, Hado P and Singh, Satinder},	1437
booktitle = {Advances in Neural Information Processing Systems},	1438
editor = {A. Oh and T. Naumann and A. Globerson and K. Saenko and M. Hardt and S. Levine},	1439
pages = {50377--50407},	1440
publisher = {Curran Associates, Inc.},	1441
title = {A Definition of Continual Reinforcement Learning},	1442
url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/9d8cf1247786d6dfeefeeb53b8b5f6d7-Paper-Conference.pdf},	1443
volume = {36},	1444
year = {2023}	1445
}	1446
	1447
@article{NGUYEN2024111566,	1448
title = {Dynamic metaheuristic selection via Thompson Sampling for online optimization},	1449
journal = {Applied Soft Computing},	1450
volume = {158},	1451
pages = {111566},	1452
year = {2024},	1453
issn = {1568-4946},	1454
doi = {https://doi.org/10.1016/j.asoc.2024.111566},	1455
url = {https://www.sciencedirect.com/science/article/pii/S1568494624003405},	1456
author = {Alain Nguyen},	1457
keywords = {Selection hyper-heuristic, Multi-armed-bandit, Thompson Sampling, Online optimization},	1458
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.}	1459
}	1460
	1461
@Article{Malladi2024,	1462
author={Malladi, Rama K.},	1463
title={Application of Supervised Machine Learning Techniques to Forecast the COVID-19 U.S. Recession and Stock Market Crash},	1464
journal={Computational Economics},	1465
year={2024},	1466
month={Mar},	1467
day={01},	1468
volume={63},	1469
number={3},	1470
pages={1021-1045},	1471
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.},	1472
issn={1572-9974},	1473
doi={10.1007/s10614-022-10333-8},	1474
url={https://doi.org/10.1007/s10614-022-10333-8}	1475
}	1476
	1477
@INPROCEEDINGS{10493943,	1478
author={Raaa Subha and Naaa Gayathri and Saaa Sasireka and Raaa Sathiyabanu and Baaa Santhiyaa and Baaa Varshini},	1479
booktitle={2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)},	1480
title={Intelligent Tutoring Systems using Long Short-Term Memory Networks and Bayesian Knowledge Tracing},	1481
year={2024},	1482
volume={0},	1483
number={0},	1484
pages={24-29},	1485
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},	1486
doi={10.1109/ICMCSI61536.2024.00010}	1487
}	1488
	1489
@article{https://doi.org/10.1155/2024/4067721,	1490
author = {Ahmed, Esmael},	1491
title = {Student Performance Prediction Using Machine Learning Algorithms},	1492
journal = {Applied Computational Intelligence and Soft Computing},	1493
volume = {2024},	1494