Scientific Publications
Conference papers/workshops
- TAILOR Scientists at IJCAI 2024 – Jeju Island
- TAILOR Selected Papers: April
- TAILOR selected papers: March
- TAILOR selected papers: January
- TAILOR scientists at AAAI2024
- TAILOR Selected Papers: December
- TAILOR Scientists at NeurIPS 2023
- TAILOR Scientists at ECAI 2023 in Krakow
- TAILOR Scientists at IJCAI 2023 in Macao
- TAILOR scientists at AAMAS and CVPR
- Accepted papers by TAILOR scientists to AAAI 2023
- TAILOR scientists at NeurIPS 2022
- Accepted papers by TAILOR scientists to IJCAI-ECAI 2022
- A TAILOR paper selected for oral presentation at CVPR 2022
- AAAI 2022: accepted papers from the TAILOR Network
All TAILOR scientific publications (on external sites)
Publications from the TAILOR project can be viewed on this dedicated Github page (opens in new tab).
TAILOR publications can also be found on OpenAire (opens in new tab).
TAILOR-related scientific publications from project start until December 2023.
[1]
A. Abate et al., ‘Rational verification: game-theoretic verification of multi-agent systems’, Appl. Intell., vol. 51, no. 9, pp. 6569–6584, 2021.
[2]
A. Abels, T. Lenaerts, V. Trianni, and A. Nowé, ‘Collective Decision-Making as a Contextual Multi-armed Bandit Problem’, in Computational Collective Intelligence – 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings, N. T. Nguyen, B. H. Hoang, C.-P. Huynh, D. Hwang, B. Trawinski, and G. Vossen, Eds., in Lecture Notes in Computer Science, vol. 12496. Springer, 2020, pp. 113–124. doi: 10.1007/978-3-030-63007-2_9.
[3]
A. Abels, T. Lenaerts, V. Trianni, and A. Nowé, ‘How Expert Confidence Can Improve Collective Decision-Making in Contextual Multi-Armed Bandit Problems’, in Computational Collective Intelligence – 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings, N. T. Nguyen, B. H. Hoang, C.-P. Huynh, D. Hwang, B. Trawinski, and G. Vossen, Eds., in Lecture Notes in Computer Science, vol. 12496. Springer, 2020, pp. 125–138. doi: 10.1007/978-3-030-63007-2_10.
[4]
A. Abels, T. Lenaerts, V. Trianni, and A. Nowé, ‘Dealing with Expert Bias in Collective Decision-Making’, CoRR, vol. abs/2106.13539, 2021, [Online]. Available: https://arxiv.org/abs/2106.13539
[5]
A. Abels, T. Lenaerts, V. Trianni, and A. Nowé, ‘Dealing with expert bias in collective decision-making’, Artif. Intell., vol. 320, p. 103921, 2023.
[6]
A. Abels, T. Lenaerts, V. Trianni, and A. Nowé, ‘Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making’, in ICML, in Proceedings of Machine Learning Research, vol. 202. PMLR, 2023, pp. 79–90.
[7]
O. Ackerman Viden, Y. Trabelsi, P. Xu, K. A. Sankararaman, O. Maksimov, and S. Kraus, ‘Allocation Problem in Remote Teleoperation: Online Matching with Offline Reusable Resources and Delayed Assignments’, in Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023, pp. 513–521.
[8]
D. M. Adamski and J. Potoniec, ‘Reason-able Embeddings: Learning Concept Embeddings with a Transferable Neural Reasoner’, Semantic Web, vol. in print, 2023.
[9]
S. Agostinelli, G. Bergami, A. Fiorenza, F. M. Maggi, A. Marrella, and F. Patrizi, ‘Discovering Declarative Process Model Behavior from Event Logs via Model Learning’, in ICPM, IEEE, 2021, pp. 48–55.
[10]
S. Agostinelli, F. Chiariello, F. M. Maggi, A. Marrella, and F. Patrizi, ‘Process mining meets model learning: Discovering deterministic finite state automata from event logs for business process analysis’, Inf. Syst., vol. 114, p. 102180, 2023, doi: 10.1016/j.is.2023.102180.
[11]
L. Ai, J. Langer, S. H. Muggleton, and U. Schmid, ‘Explanatory machine learning for sequential human teaching’, Machine Learning, 2023, doi: 10.1007/s10994-023-06351-8.
[12]
D. Aineto, S. Jiménez, and E. Onaindia, ‘Generalized Temporal Inference via Planning’, in KR, 2021, pp. 22–31.
[13]
D. Aineto, E. Onaindia, M. Ramírez, E. Scala, and I. Serina, ‘Explaining the Behaviour of Hybrid Systems with PDDL+ Planning’, in IJCAI, ijcai.org, 2022, pp. 4567–4573.
[14]
D. Albani, W. Hönig, D. Nardi, N. Ayanian, and V. Trianni, ‘Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms’, Applied Sciences, vol. 11, no. 7, p. 3115, 2021, doi: 10.3390/app11073115.
[15]
N. Alechina, G. D. Giacomo, B. Logan, and G. Perelli, ‘Automatic Synthesis of Dynamic Norms for Multi-Agent Systems’, in KR, 2022.
[16]
A. Alman, F. M. Maggi, M. Montali, F. Patrizi, and A. Rivkin, ‘Multi-Model Monitoring Framework for Hybrid Process Specifications’, in CAISE, 2022.
[17]
M. Alpuente, A. Cuenca-Ortega, S. Escobar, and J. Meseguer, ‘Order-sorted Homeomorphic Embedding Modulo Combinations of Associativity and/or Commutativity Axioms’, Fundam. Informaticae, vol. 177, no. 3–4, pp. 297–329, 2020, doi: 10.3233/FI-2020-1991.
[18]
M. Alpuente, D. Pardo, and A. Villanueva, ‘Abstract Contract Synthesis and Verification in the Symbolic K Framework’, Fundam. Informaticae, vol. 177, no. 3–4, pp. 235–273, 2020, doi: 10.3233/FI-2020-1989.
[19]
S. Alsaidi et al., ‘An analogy based framework for patient-stay identification in healthcare’, in ATA@ICCBR 2022 – Workshop Analogies: from Theory to Applications, Nancy, France, 2022. [Online]. Available: https://hal.inria.fr/hal-03763772
[20]
S. Alsaidi, M. Couceiro, S. Quennelle, A. Burgun, N. Garcelon, and A. Coulet, ‘Exploring Analogical Inference in Healthcare’, in Proceedings of the Workshop on the Interactions between Analogical Reasoning and Machine Learning (International Joint Conference on Artificial Intelligence – European Conference on Artificial Intelligence (IJCAI-ECAI 2022)), Vienna, Austria, July 23, 2022, 2022, pp. 40–50.
[21]
S. Alsaidi, A. Decker, P. Lay, E. Marquer, P.-A. Murena, and M. Couceiro, ‘A Neural Approach for Detecting Morphological Analogies’, in The 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA), Porto/Online, Portugal, 2021. [Online]. Available: https://hal.inria.fr/hal-03313556
[22]
G. Alves, M. Amblard, F. Bernier, M. Couceiro, and A. Napoli, ‘Reducing Unintended Bias of ML Models on Tabular and Textual Data’, in The 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA), Porto/Online, Portugal, 2021. [Online]. Available: https://hal.archives-ouvertes.fr/hal-03312797
[23]
G. Alves, V. Bhargava, M. Couceiro, and A. Napoli, ‘Making ML Models Fairer Through Explanations: The Case of LimeOut’, in Analysis of Images, Social Networks and Texts – 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15-16, 2020, Revised Selected Papers, in Lecture Notes in Computer Science, vol. 12602. Springer, 2020, pp. 3–18.
[24]
L. R. Amado, R. F. Pereira, and F. Meneguzzi, ‘Combining LSTMs and Symbolic Approaches for Robust Plan Recognition’, in AAMAS, 2021.
[25]
U. Amato, A. Antoniadis, I. D. Feis, and I. Gijbels, ‘Penalized wavelet nonparametric univariate logistic regression for irregular spaced data’, Statistics, vol. 0, no. 0, pp. 1–24, 2023, doi: 10.1080/02331888.2023.2248679.
[26]
B. Aminof, G. D. Giacomo, A. Lomuscio, A. Murano, and S. Rubin, ‘Synthesizing Best-Effort Strategies under Multiple Environment Specifications’, in KR, 2021.
[27]
B. Aminof, G. D. Giacomo, and S. Rubin, ‘Best-Effort Synthesis: Doing Your Best Is Not Harder Than Giving Up’, in IJCAI, 2021.
[28]
B. Aminof, G. D. Giacomo, S. Rubin, and F. Zuleger, ‘Beyond Strong-Cyclic: Doing Your Best in Stochastic Environments’, in IJCAI, ijcai.org, 2022, pp. 2525–2531.
[29]
B. Aminof, G. D. Giacomo, and S. Rubin, ‘Reactive Synthesis of Dominant Strategies’, in AAAI, AAAI Press, 2023, pp. 6228–6235.
[30]
B. Aminof, G. D. Giacomo, S. Rubin, and F. Zuleger, ‘Stochastic Best-Effort Strategies for Borel Goals’, in LICS, 2023, pp. 1–13.
[31]
B. Aminof, G. D. Giacomo, A. D. Stasio, H. Francon, S. Rubin, and S. Zhu, ‘LTLf Synthesis Under Environment Specifications for Reachability and Safety Properties’, CoRR, vol. abs/2308.15184, 2023.
[32]
B. Aminof, G. D. Giacomo, A. D. Stasio, H. Francon, S. Rubin, and S. Zhu, ‘sc ltlf Synthesis Under Environment Specifications for Reachability and Safety Properties’, in EUMAS, in Lecture Notes in Computer Science, vol. 14282. Springer, 2023, pp. 263–279.
[33]
G. Apriceno, A. Passerini, and L. Serafini, ‘A Neuro-Symbolic Approach to Structured Event Recognition’, in 28th International Symposium on Temporal Representation and Reasoning (TIME 2021), in Leibniz International Proceedings in Informatics (LIPIcs), vol. 206. 2021, p. 11:1-11:14. doi: 10.4230/LIPIcs.TIME.2021.11.
[34]
H. Asghar, C. Bobineau, and M.-C. Rousset, ‘Identifying Privacy Risks raised by Utility Queries’, in 23rd International conference on Web Information Systems Engineering (WISE 2022), Biarritz, France, Oct. 2022. [Online]. Available: https://hal.archives-ouvertes.fr/hal-03833542
[35]
H. Asghar, C. Bobineau, and M.-C. Rousset, ‘Explanation-based Tool for Helping Data Producers to Reduce Privacy Risks’, in Extended Semantic Web Conference, Heraklion, Greece: Jamie McCusker and Ernesto Jimenez-Ruiz, May 2023. [Online]. Available: https://hal.science/hal-04215771
[36]
G. Attardi, D. Sartiano, and M. Simi, ‘Biaffine Dependency and Semantic Graph Parsing for EnhancedUniversal Dependencies’, in Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021), Online: Association for Computational Linguistics, Aug. 2021, pp. 184–188. doi: 10.18653/v1/2021.iwpt-1.19.
[37]
D. Atzeni, D. Bacciu, F. Errica, and A. Micheli, ‘Modeling Edge Features with Deep Bayesian Graph Networks’, in International Joint Conference on Neural Networks, IJCNN 2021, Shenzhen, China, July 18-22, 2021, IEEE, 2021, pp. 1–8. doi: 10.1109/IJCNN52387.2021.9533430.
[38]
G. Audemard, S. Bellart, L. Bounia, F. Koriche, J.-M. Lagniez, and P. Marquis, ‘On the Computational Intelligibility of Boolean Classifiers’, in KR, 2021, pp. 74–86.
[39]
G. Audemard, S. Bellart, L. Bounia, F. Koriche, J.-M. Lagniez, and P. Marquis, ‘On Preferred Abductive Explanations for Decision Trees and Random Forests’, in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, ijcai.org, 2022, pp. 643–650.
[40]
G. Audemard, S. Bellart, L. Bounia, F. Koriche, J.-M. Lagniez, and P. Marquis, ‘Trading Complexity for Sparsity in Random Forest Explanations’, in Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 – March 1, 2022, AAAI Press, 2022, pp. 5461–5469.
[41]
S. Azzolin, A. Longa, P. Barbiero, P. Lio, and A. Passerini, ‘Global Explainability of GNNs via Logic Combination of Learned Concepts’, in The Eleventh International Conference on Learning Representations, 2023.
[42]
D. Azzolini, E. Bellodi, and F. Riguzzi, ‘Approximate Inference in Probabilistic Answer Set Programming for Statistical Probabilities’, in AI*IA, in Lecture Notes in Computer Science, vol. 13796. Springer, 2022, pp. 33–46.
[43]
D. Azzolini, E. Bellodi, and F. Riguzzi, ‘MAP Inference in Probabilistic Answer Set Programs’, in AI*IA, in Lecture Notes in Computer Science, vol. 13796. Springer, 2022, pp. 413–426.
[44]
D. Azzolini, E. Bellodi, and F. Riguzzi, ‘Statistical Statements in Probabilistic Logic Programming’, in LPNMR, in Lecture Notes in Computer Science, vol. 13416. Springer, 2022, pp. 43–55.
[45]
D. Azzolini and F. Riguzzi, ‘Probabilistic Logic Models for the Lightning Network’, Cryptogr., vol. 6, no. 2, p. 29, 2022.
[46]
D. Azzolini and F. Riguzzi, ‘Inference in Probabilistic Answer Set Programming under the Credal Semantics’, in AixIA 2023 – Advances in Artificial Intelligence, in Lecture Notes in Artificial Intelligence. Heidelberg, Germany: Springer, 2023.
[47]
D. Azzolini and F. Riguzzi, ‘Lifted Inference for Statistical Statements in Probabilistic Answer Set Programming’, International Journal of Approximate Reasoning, 2023.
[48]
J. B\laszczyński, S. Greco, B. Matarazzo, and M. Szeląg, ‘Dominance-Based Rough Set Approach: Basic Ideas and Main Trends’, in Intelligent Decision Support Systems: Combining Operations Research and Artificial Intelligence – Essays in Honor of Roman S\lowiński, in Multiple Criteria Decision Making. , Cham: Springer, 2022, pp. 353–382. doi: 10.1007/978-3-030-96318-7_18.
[49]
D. Bacciu, A. Conte, R. Grossi, F. Landolfi, and A. Marino, ‘K-plex cover pooling for graph neural networks’, Data Min. Knowl. Discov., vol. 35, no. 5, pp. 2200–2220, 2021, doi: 10.1007/s10618-021-00779-z.
[50]
D. Bacciu, A. Conte, and F. Landolfi, ‘Generalizing Downsampling from Regular Data to Graphs’, in AAAI, AAAI Press, 2023, pp. 6718–6727. doi: 10.1609/aaai.v37i6.25824.
[51]
D. Bacciu, F. Errica, A. Gravina, L. Madeddu, M. Podda, and G. Stilo, ‘Deep Graph Networks for Drug Repurposing with Multi-Protein Targets’, IEEE Transactions on Emerging Topics in Computing, pp. 1–14, 2023, doi: 10.1109/TETC.2023.3238963.
[52]
D. Bacciu and D. Numeroso, ‘Explaining Deep Graph Networks via Input Perturbation’, IEEE Transactions on Neural Networks and Learning Systems, pp. 1–12, 2022, doi: 10.1109/TNNLS.2022.3165618.
[53]
D. Bacciu and M. Podda, ‘Graphgen-redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation’, in International Joint Conference on Neural Networks, IJCNN 2021, Shenzhen, China, July 18-22, 2021, IEEE, 2021, pp. 1–8. doi: 10.1109/IJCNN52387.2021.9533743.
[54]
B. Banihashemi, G. D. Giacomo, and Y. Lespérance, ‘Abstraction of Nondeterministic Situation Calculus Action Theories’, in IJCAI, ijcai.org, 2023, pp. 3112–3122.
[55]
B. Banihashemi, G. D. Giacomo, and Y. Lespérance, ‘Abstraction of Nondeterministic Situation Calculus Action Theories – Extended Version’, CoRR, vol. abs/2305.14222, 2023.
[56]
P. Baquero-Arnal et al., ‘MLLP-VRAIN Spanish ASR Systems for the Albayzin-RTVE 2020 Speech-To-Text Challenge: Extension’, Applied Sciences, vol. 12, no. 2, p. 804, 2022, doi: 10.3390/app12020804.
[57]
P. Barbiero et al., ‘Interpretable Neural-Symbolic Concept Reasoning’, in ICML, in Proceedings of Machine Learning Research, vol. 202. PMLR, 2023, pp. 1801–1825. [Online]. Available: https://proceedings.mlr.press/v202/barbiero23a.html
[58]
R. Barták, S. Ondrcková, G. Behnke, and P. Bercher, ‘Correcting Hierarchical Plans by Action Deletion’, in KR, 2021, pp. 99–109. doi: 10.24963/kr.2021/10.
[59]
A. E. Baz et al., ‘Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification’, in NeurIPS (Competition and Demos), in Proceedings of Machine Learning Research, vol. 176. PMLR, 2021, pp. 80–96.
[60]
A. Bazin, M. Couceiro, M.-D. Devignes, and A. Napoli, ‘Explaining Multicriteria Decision Making with Formal Concept Analysis’, in Proceedings of the Fifthteenth International Conference on Concept Lattices and Their Applications, Tallinn, Estonia, June 29-July 1, 2020, in CEUR Workshop Proceedings, vol. 2668. CEUR-WS.org, 2020, pp. 119–130.
[61]
A. Bazin, M. Couceiro, M.-D. Devignes, and A. Napoli, ‘Steps towards causal Formal Concept Analysis’, Int. J. Approx. Reason., vol. 142, pp. 338–348, 2022.
[62]
N. Beckers, L. Cavalcante Siebert, M. Bruijnes, C. Jonker, and D. Abbink, ‘Drivers of partially automated vehicles are blamed for crashes that they cannot reasonably avoid’, Nature Scientific Reports, vol. 12, no. 16193, 2022.
[63]
M. Behery, M. Trinh, C. Brecher, and G. Lakemeyer, ‘Self-Optimizing Agents Using Mixed Initiative Behavior Trees’, in SEAMS, IEEE, 2023, pp. 97–103. [Online]. Available: https://doi.org/10.1109/SEAMS59076.2023.00023
[64]
G. Behnke, D. Speck, M. Katz, and S. Sohrabi, ‘On Partial Satisfaction Planning with Total-Order HTNs’, in ICAPS, 2023, pp. 42–51.
[65]
M.-B. Belaid, N. Belmecheri, A. Gotlieb, N. Lazaar, and H. Spieker, ‘GEQCA: Generic Qualitative Constraint Acquisition’, in AAAI, 2022.
[66]
J. Berg, B. Bogaerts, J. Nordström, A. Oertel, and D. Vandesande, ‘Certified Core-Guided MaxSAT Solving’, in CADE, in Lecture Notes in Computer Science, vol. 14132. Springer, 2023, pp. 1–22. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-031-38499-8_1
[67]
M. Bernaschi et al., ‘Seeking critical nodes in digraphs’, Journal of Computational Science, vol. 69, p. 102012, 2023, doi: https://doi.org/10.1016/j.jocs.2023.102012.
[68]
C. Bessiere, C. Carbonnel, M. C. Cooper, and E. Hebrard, ‘Complexity of Minimum-Size Arc-Inconsistency Explanations’, in 28th International Conference on Principles and Practice of Constraint Programming, CP 2022, July 31 to August 8, 2022, Haifa, Israel, in LIPIcs, vol. 235. Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2022, p. 9:1-9:14. doi: 10.4230/LIPIcs.CP.2022.9.
[69]
C. Bessiere et al., ‘Learning constraints through partial queries’, Artif. Intell., vol. 319, p. 103896, 2023.
[70]
C. Bessiere, C. Carbonnel, and A. Himeur, ‘Learning Constraint Networks over Unknown Constraint Languages’, in IJCAI, ijcai.org, 2023, pp. 1876–1883.
[71]
V. Bhargava, M. Couceiro, and A. Napoli, ‘LimeOut: An Ensemble Approach to Improve Process Fairness’, in ECML PKDD 2020 Workshops – Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, DINA 2020, EDML 2020, XKDD 2020 and INRA 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, in Communications in Computer and Information Science, vol. 1323. Springer, 2020, pp. 475–491.
[72]
A. Biedenkapp, H. F. Bozkurt, T. Eimer, F. Hutter, and M. Lindauer, ‘Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework’, in ECAI, in Frontiers in Artificial Intelligence and Applications, vol. 325. IOS Press, 2020, pp. 427–434.
[73]
A. Biedenkapp, D. Speck, S. Sievers, F. Hutter, M. Lindauer, and J. Seipp, ‘Learning Domain-Independent Policies for Open List Selection’, in PRL Workshop, 2022.
[74]
B. Bischl et al., ‘OpenML Benchmarking Suites’, in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, in NIPS’21. 2021.
[75]
B. Bischl et al., ‘OpenML Benchmarking Suites’, in Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021.
[76]
I. Błądek and K. Krawiec, ‘Counterexample-Driven Genetic Programming for Symbolic Regression with Formal Constraints’, IEEE Transactions on Evolutionary Computation, vol. 26, no. 6, pp. 1–1, 2022, doi: 10.1109/TEVC.2022.3205286.
[77]
I. Bleukx, J. Devriendt, E. Gamba, B. Bogaerts, and T. Guns, ‘Simplifying Step-Wise Explanation Sequences’, in CP, in LIPIcs, vol. 280. Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2023, p. 11:1-11:20.
[78]
M. Blsták and V. Rozinajová, ‘Automatic question generation based on sentence structure analysis using machine learning approach’, Nat. Lang. Eng., vol. 28, no. 4, pp. 487–517, 2022.
[79]
F. Bodria, F. Giannotti, R. Guidotti, F. Naretto, D. Pedreschi, and S. Rinzivillo, ‘Benchmarking and survey of explanation methods for black box models’, Data Mining and Knowledge Discovery, vol. 37, no. 5, pp. 1719–1778, 2023, doi: 10.1007/s10618-023-00933-9.
[80]
B. Bogaerts, S. Gocht, C. McCreesh, and J. Nordström, ‘Certified Dominance and Symmetry Breaking for Combinatorial Optimisation’, J. Artif. Intell. Res., vol. 77, pp. 1539–1589, 2023.
[81]
J. Bogatinovski, L. Todorovski, S. Dzeroski, and D. Kocev, ‘Comprehensive comparative study of multi-label classification methods’, Expert Syst. Appl., vol. 203, p. 117215, 2022, doi: 10.1016/j.eswa.2022.117215.
[82]
J. Bogatinovski, L. Todorovski, S. Dzeroski, and D. Kocev, ‘Explaining the performance of multilabel classification methods with data set properties’, Int. J. Intell. Syst., vol. 37, no. 9, pp. 6080–6122, 2022, doi: 10.1002/int.22835.
[83]
K. Boggess, S. Kraus, and L. Feng, ‘Toward policy explanations for multi-agent reinforcement learning’, Proceedings of IJCAI-22, 2022.
[84]
K. Boggess, S. Kraus, and L. Feng, ‘Explainable Multi-Agent Reinforcement Learning for Temporal Queries’, Proceedings of IJCAI-23, 2023.
[85]
D. van Bokkem, M. van den Hemel, S. Dumancic, and N. Yorke-Smith, ‘Embedding a Long Short-Term Memory Network in a Constraint Programming Framework for Tomato Greenhouse Optimisation’, in AAAI, AAAI Press, 2023, pp. 15731–15737. doi: 10.1609/aaai.v37i13.26867.
[86]
L. Bonassi, G. De Giacomo, M. Favorito, F. Fuggitti, A. Gerevini, and E. Scala, ‘FOND Planning for Pure-Past Linear Temporal Logic Goals’, Proceedings of the 26th European Conference on Artificial Intelligence (ECAI 2023), Sep. 2023, doi: 10.3233/FAIA230281.
[87]
L. Bonassi, G. D. Giacomo, M. Favorito, F. Fuggitti, A. E. Gerevini, and E. Scala, ‘Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic’, in ICAPS, AAAI Press, 2023, pp. 61–69.
[88]
B. Bonet and H. Geffner, ‘General Policies, Representations, and Planning Width’, in AAAI, 2021.
[89]
V. Bonsignori, R. Guidotti, and A. Monreale, ‘Deriving a Single Interpretable Model by Merging Tree-Based Classifiers’, in Discovery Science – 24th International Conference, DS 2021, Halifax, NS, Canada, October 11-13, 2021, Proceedings, in Lecture Notes in Computer Science, vol. 12986. Springer, 2021, pp. 347–357. doi: 10.1007/978-3-030-88942-5_27.
[90]
A. Bontempelli, F. Giunchiglia, A. Passerini, and S. Teso, ‘Human-in-the-loop Handling of Knowledge Drift’, Data Mining and Knowledge Discovery, 2022.
[91]
A. Bontempelli, F. Giunchiglia, A. Passerini, and S. Teso, ‘Toward a Unified Framework for Debugging Gray-box Models’, The AAAI-22 Workshop on Interactive Machine Learning, 2022.
[92]
A. Bontempelli, S. Teso, K. Tentori, F. Giunchiglia, and A. Passerini, ‘Concept-level Debugging of Part-Prototype Networks’, in The Eleventh International Conference on Learning Representations, 2023.
[93]
A. W. Bosman, H. H. Hoos, and J. N. van Rijn, ‘A Preliminary Study of Critical Robustness Distributions in Neural Network Verification’, 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), 2023.
[94]
J. Boudou, A. Herzig, and N. Troquard, ‘Resource separation in dynamic logic of propositional assignments’, J. Log. Algebraic Methods Program., vol. 121, p. 100683, 2021, doi: 10.1016/j.jlamp.2021.100683.
[95]
H. Bourel, A. Jonsson, O.-A. Maillard, and M. S. Talebi, ‘Exploration in Reward Machines with Low Regret’, in International Conference on Artificial Intelligence and Statistics (AISTATS), 2023, pp. 4114–4146.
[96]
D. Brunori, S. Colonnese, F. Cuomo, and L. Iocchi, ‘A Reinforcement Learning Environment for Multi-Service UAV-enabled Wireless Systems’, 2021.
[97]
D. Brzezinski, L. L. Minku, T. Pewinski, J. Stefanowski, and A. Szumaczuk, ‘The impact of data difficulty factors on classification of imbalanced and concept drifting data streams’, Knowl. Inf. Syst., vol. 63, no. 6, pp. 1429–1469, 2021, doi: 10.1007/s10115-021-01560-w.
[98]
J. Burden, J. Hernández-Orallo, and S. Ó. hÉigeartaigh, ‘Negative Side Effects and AI Agent Indicators: Experiments in SafeLife’, in SafeAI@AAAI, 2021.
[99]
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