Scientific Publications

Conference papers/workshops

TAILOR-related scientific publications from project start until December 2022.

  1. Abate, A. et al. (2021) ‘Rational verification: game-theoretic verification of multi-agent systems’, Appl. Intell., 51(9), pp. 6569–6584.
  2. Abels, A. et al. (2020a) ‘Collective Decision-Making as a Contextual Multi-armed Bandit Problem’, in N.T. Nguyen et al. (eds) Computational Collective Intelligence – 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings. Springer (Lecture Notes in Computer Science), pp. 113–124. Available at: https://doi.org/10.1007/978-3-030-63007-2_9.
  3. Abels, A. et al. (2020b) ‘How Expert Confidence Can Improve Collective Decision-Making in Contextual Multi-Armed Bandit Problems’, in N.T. Nguyen et al. (eds) Computational Collective Intelligence – 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings. Springer (Lecture Notes in Computer Science), pp. 125–138. Available at: https://doi.org/10.1007/978-3-030-63007-2_10.
  4. Abels, A. et al. (2021) ‘Dealing with Expert Bias in Collective Decision-Making’, CoRR, abs/2106.13539. Available at: https://arxiv.org/abs/2106.13539.
  5. Agostinelli, S. et al. (2021) ‘Discovering Declarative Process Model Behavior from Event Logs via Model Learning’, in ICPM. IEEE, pp. 48–55.
  6. Aineto, D. et al. (2022) ‘Explaining the Behaviour of Hybrid Systems with PDDL+ Planning’, in IJCAI. ijcai.org, pp. 4567–4573.
  7. Aineto, D., Jiménez, S. and Onaindia, E. (2021) ‘Generalized Temporal Inference via Planning’, in KR, pp. 22–31.
  8. Albani, D. et al. (2021) ‘Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms’, Applied Sciences, 11(7), p. 3115. Available at: https://doi.org/10.3390/app11073115.
  9. Alechina, N. et al. (2022) ‘Automatic Synthesis of Dynamic Norms for Multi-Agent Systems’, in KR.
  10. Alman, A. et al. (2022) ‘Multi-Model Monitoring Framework for Hybrid Process Specifications’, in CAISE.
  11. Alpuente, M. et al. (2020) ‘Order-sorted Homeomorphic Embedding Modulo Combinations of Associativity and/or Commutativity Axioms’, Fundam. Informaticae, 177(3–4), pp. 297–329. Available at: https://doi.org/10.3233/FI-2020-1991.
  12. Alpuente, M., Pardo, D. and Villanueva, A. (2020) ‘Abstract Contract Synthesis and Verification in the Symbolic K Framework’, Fundam. Informaticae, 177(3–4), pp. 235–273. Available at: https://doi.org/10.3233/FI-2020-1989.
  13. Alsaidi, S. et al. (2021) ‘A Neural Approach for Detecting Morphological Analogies’, in The 8th IEEE International Conference on Data Science and Advanced Analytics (DSAA). Porto/Online, Portugal. Available at: https://hal.inria.fr/hal-03313556.
  14. Alsaidi, S., Couceiro, M., Marquer, E., et al. (2022) ‘An analogy based framework for patient-stay identification in healthcare’, in ATA@ICCBR 2022 – Workshop Analogies: from Theory to Applications. Nancy, France. Available at: https://hal.inria.fr/hal-03763772.
  15. Alsaidi, S., Couceiro, M., Quennelle, S., et al. (2022) ‘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, pp. 40–50.
  16. Alves, G. et al. (2020) ‘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. Springer (Lecture Notes in Computer Science), pp. 3–18.
  17. Alves, G. et al. (2021) ‘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. Available at: https://hal.archives-ouvertes.fr/hal-03312797.
  18. Amado, L.R., Pereira, R.F. and Meneguzzi, F. (2021) ‘Combining LSTMs and Symbolic Approaches for Robust Plan Recognition’, in AAMAS.
  19. Aminof, B. et al. (2021) ‘Synthesizing Best-Effort Strategies under Multiple Environment Specifications’, in KR.
  20. Aminof, B. et al. (2022) ‘Beyond Strong-Cyclic: Doing Your Best in Stochastic Environments’, in IJCAI. ijcai.org, pp. 2525–2531.
  21. Aminof, B., Giacomo, G.D. and Rubin, S. (2021) ‘Best-Effort Synthesis: Doing Your Best Is Not Harder Than Giving Up’, in IJCAI.
  22. Apriceno, G., Passerini, A. and Serafini, L. (2021) ‘A Neuro-Symbolic Approach to Structured Event Recognition’, in 28th International Symposium on Temporal Representation and Reasoning (TIME 2021). (Leibniz International Proceedings in Informatics (LIPIcs)), p. 11:1-11:14. Available at: https://doi.org/10.4230/LIPIcs.TIME.2021.11.
  23. Attardi, G., Sartiano, D. and Simi, M. (2021) ‘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, pp. 184–188. Available at: https://doi.org/10.18653/v1/2021.iwpt-1.19.
  24. Atzeni, D. et al. (2021) ‘Modeling Edge Features with Deep Bayesian Graph Networks’, in International Joint Conference on Neural Networks, IJCNN 2021, Shenzhen, China, July 18-22, 2021. IEEE, pp. 1–8. Available at: https://doi.org/10.1109/IJCNN52387.2021.9533430.
  25. Audemard, G. et al. (2021) ‘On the Computational Intelligibility of Boolean Classifiers’, in KR, pp. 74–86.
  26. Audemard, G. et al. (2022a) ‘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, pp. 643–650.
  27. Audemard, G. et al. (2022b) ‘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, pp. 5461–5469.
  28. Azzolini, D., Bellodi, E. and Riguzzi, F. (2022) ‘Statistical Statements in Probabilistic Logic Programming’, in LPNMR. Springer (Lecture Notes in Computer Science), pp. 43–55.
  29. Azzolini, D. and Riguzzi, F. (2022) ‘Probabilistic Logic Models for the Lightning Network’, Cryptogr., 6(2), p. 29.
  30. Bacciu, D. et al. (2021a) ‘K-plex cover pooling for graph neural networks’, Data Min. Knowl. Discov., 35(5), pp. 2200–2220.
  31. Bacciu, D. et al. (2021b) ‘K-plex cover pooling for graph neural networks’, Data Min. Knowl. Discov., 35(5), pp. 2200–2220. Available at: https://doi.org/10.1007/s10618-021-00779-z.
  32. Bacciu, D. and Podda, M. (2021) ‘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, pp. 1–8. Available at: https://doi.org/10.1109/IJCNN52387.2021.9533743.
  33. Baquero-Arnal, P. et al. (2022) ‘MLLP-VRAIN Spanish ASR Systems for the Albayzin-RTVE 2020 Speech-To-Text Challenge: Extension’, Applied Sciences, 12(2), p. 804. Available at: https://doi.org/10.3390/app12020804.
  34. Barták, R. et al. (2021) ‘Correcting Hierarchical Plans by Action Deletion’, in KR, pp. 99–109. Available at: https://doi.org/10.24963/kr.2021/10.
  35. Baz, A.E. et al. (2021) ‘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). PMLR (Proceedings of Machine Learning Research), pp. 80–96.
  36. Bazin, A. et al. (2020) ‘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. CEUR-WS.org (CEUR Workshop Proceedings), pp. 119–130.
  37. Bazin, A. et al. (2022) ‘Steps towards causal Formal Concept Analysis’, Int. J. Approx. Reason., 142, pp. 338–348.
  38. Beckers, N. et al. (2022) ‘Drivers of partially automated vehicles are blamed for crashes that they cannot reasonably avoid’, Nature Scientific Reports, 12(16193).
  39. Belaid, M.-B. et al. (2022) ‘GEQCA: Generic Qualitative Constraint Acquisition’, in AAAI.
  40. Bessiere, C. et al. (2022) ‘Complexity of Minimum-Size Arc-Inconsistency Explanations’, in C. Solnon (ed.) 28th International Conference on Principles and Practice of Constraint Programming, CP 2022, July 31 to August 8, 2022, Haifa, Israel. Schloss Dagstuhl – Leibniz-Zentrum für Informatik (LIPIcs), p. 9:1-9:14. Available at: https://doi.org/10.4230/LIPIcs.CP.2022.9.
  41. Bhargava, V., Couceiro, M. and Napoli, A. (2020) ‘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. Springer (Communications in Computer and Information Science), pp. 475–491.
  42. Biedenkapp, A. et al. (2020) ‘Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework’, in ECAI. IOS Press (Frontiers in Artificial Intelligence and Applications), pp. 427–434.
  43. Bischl, B. et al. (2021) ‘OpenML Benchmarking Suites’, in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks. (NIPS’21).
  44. Bischl, Bernd et al. (2021) ‘OpenML Benchmarking Suites’, in Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).
  45. Błądek, I. and Krawiec, K. (2022) ‘Counterexample-Driven Genetic Programming for Symbolic Regression with Formal Constraints’, IEEE Transactions on Evolutionary Computation, 26(6), pp. 1–1. Available at: https://doi.org/10.1109/TEVC.2022.3205286.
  46. B\laszczyński, J. et al. (2022) ‘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. Cham: Springer (Multiple Criteria Decision Making), pp. 353–382. Available at: https://doi.org/10.1007/978-3-030-96318-7_18.
  47. van der Blom, K. et al. (2021) ‘AutoML Adoption in ML Software’, in 8th ICML Workshop on Automated Machine Learning.
  48. van der Blom, K. et al. (2022) ‘Sparkle: Towards Accessible Meta-Algorithmics for Improving the State of the Art in Solving Challenging Problems’, IEEE Transactions on Evolutionary Computation [Preprint]. Available at: https://doi.org/10.1109/TEVC.2022.3215013.
  49. Blsták, M. and Rozinajová, V. (2022) ‘Automatic question generation based on sentence structure analysis using machine learning approach’, Nat. Lang. Eng., 28(4), pp. 487–517.
  50. Bogatinovski, J. et al. (2022a) ‘Comprehensive comparative study of multi-label classification methods’, Expert Syst. Appl., 203, p. 117215. Available at: https://doi.org/10.1016/j.eswa.2022.117215.
  51. Bogatinovski, J. et al. (2022b) ‘Explaining the performance of multilabel classification methods with data set properties’, Int. J. Intell. Syst., 37(9), pp. 6080–6122. Available at: https://doi.org/10.1002/int.22835.
  52. van Bokkem, D. et al. (2023) ‘Embedding a Long Short-Term Memory Network in a Constraint Programming Framework for Tomato Greenhouse Optimisation’, in Proceedings of the 35th International Conference on Innovative Applications of Artificial Intelligence (IAAI’23). Washington, DC, USA.
  53. Bonet, B. and Geffner, H. (2021) ‘General Policies, Representations, and Planning Width’, in AAAI.
  54. Bonsignori, V., Guidotti, R. and Monreale, A. (2021) ‘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. Springer (Lecture Notes in Computer Science), pp. 347–357. Available at: https://doi.org/10.1007/978-3-030-88942-5_27.
  55. Bontempelli, A. et al. (2022a) ‘Human-in-the-loop Handling of Knowledge Drift’, Data Mining and Knowledge Discovery [Preprint].
  56. Bontempelli, A. et al. (2022b) ‘Toward a Unified Framework for Debugging Gray-box Models’, The AAAI-22 Workshop on Interactive Machine Learning [Preprint].
  57. Boudou, J., Herzig, A. and Troquard, N. (2021) ‘Resource separation in dynamic logic of propositional assignments’, J. Log. Algebraic Methods Program., 121, p. 100683. Available at: https://doi.org/10.1016/j.jlamp.2021.100683.
  58. Brunori, D. et al. (2021) ‘A Reinforcement Learning Environment for Multi-Service UAV-enabled Wireless Systems’.
  59. Brzezinski, D. et al. (2021) ‘The impact of data difficulty factors on classification of imbalanced and concept drifting data streams’, Knowl. Inf. Syst., 63(6), pp. 1429–1469. Available at: https://doi.org/10.1007/s10115-021-01560-w.
  60. Büchner, C., Keller, T. and Helmert, M. (2021) ‘Exploiting Cyclic Dependencies in Landmark Heuristics’, in ICAPS.
  61. Burden, J., Hernández-Orallo, J. and hÉigeartaigh, S.Ó. (2021) ‘Negative Side Effects and AI Agent Indicators: Experiments in SafeLife’, in SafeAI@AAAI.
  62. Calautti, M., Console, M. and Pieris, A. (2021) ‘Benchmarking Approximate Consistent Query Answering’, in L. Libkin, R. Pichler, and P. Guagliardo (eds) PODS’21: Proceedings of the 40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, Virtual Event, China, June 20-25, 2021. ACM, pp. 233–246. Available at: https://doi.org/10.1145/3452021.3458309.
  63. Calvanese, D. et al. (2022) ‘Verification and Monitoring for First-Order LTL with Persistence-Preserving Quantification over Finite and Infinite Traces’, in IJCAI. ijcai.org, pp. 2553–2560.
  64. Cardillo, F.A. and Straccia, U. (2021) ‘Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued Boosting’, Fuzzy Sets and Systems [Preprint]. Available at: https://doi.org/10.1016/j.fss.2021.07.002.
  65. Casini, G., Meyer, T.A. and Varzinczak, I. (2021) ‘Contextual Conditional Reasoning’, in AAAI. AAAI Press, pp. 6254–6261.
  66. Casini, G. and Straccia, U. (2022a) ‘A General Framework for Modelling Conditional Reasoning – Preliminary Report’, in Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning, pp. 112–121. Available at: https://doi.org/10.24963/kr.2022/12.
  67. Casini, G. and Straccia, U. (2022b) ‘A Rational Entailment for Expressive Description Logics via Description Logic Programs’, in Proceedings of the Southern African Artificial Intelligence Conference (SACAIR-2021). Springer (Communications in Computer and Information Science), pp. 177–191. Available at: https://doi.org/10.1007/978-3-030-95070-5_12.
  68. Castellana, D. and Bacciu, D. (2020) ‘Learning from Non-Binary Constituency Trees via Tensor Decomposition’, in Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020. International Committee on Computational Linguistics, pp. 3899–3910. Available at: https://doi.org/10.18653/v1/2020.coling-main.346.
  69. Castellana, D. and Bacciu, D. (2022) ‘A tensor framework for learning in structured domains’, Neurocomputing, 470, pp. 405–426. Available at: https://doi.org/10.1016/j.neucom.2021.05.110.
  70. Čepek, O. (2022) ‘Switch lists in the landscape of knowledge representation languages’, in R. Barták, F. Keshtkar, and M. Franklin (eds) Proceedings of the Thirty-Fifth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2022, Hutchinson Island, Jensen Beach, Florida, USA, May 15-18, 2022. Available at: https://doi.org/10.32473/flairs.v35i.130700.
  71. Čepek, O. and Weigle, J. (2022) ‘A Direct Equivalence-Testing Algorithm for SLRs’, in Proceedings of the International Symposium on Artificial Intelligence and Mathematics 2022 (ISAIM 2022), Fort Lauderdale, Florida, USA, January 3-5, 2022. Available at: https://isaim2022.cs.ou.edu/papers/ISAIM2022_Boolean_Cepek_Weigle.pdf.
  72. Chan, K. et al. (2022) ‘Solving Morphological Analogies Through Generation’, 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, pp. 29–39.
  73. Chiariello, F., Maggi, F.M. and Patrizi, F. (2022) ‘ASP-Based Declarative Process Mining’, in AAAI.
  74. Chicano, F. et al. (2021) ‘Dynastic Potential Crossover Operator’, Evolutionary Computation, pp. 1–35. Available at: https://doi.org/10.1162/evco_a_00305.
  75. Christen, R. et al. (2022) ‘Detecting Unsolvability Based on Separating Functions’, in ICAPS, pp. 44–52.
  76. Cima, G. et al. (2021) ‘Abstraction in Data Integration’, in 36th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS 2021, Rome, Italy, June 29 – July 2, 2021. IEEE, pp. 1–11. Available at: https://doi.org/10.1109/LICS52264.2021.9470716.
  77. Cima, G., Croce, F. and Lenzerini, M. (2021) ‘Query Definability and Its Approximations in Ontology-based Data Management’, in G. Demartini et al. (eds) CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 – 5, 2021. ACM, pp. 271–280. Available at: https://doi.org/10.1145/3459637.3482466.
  78. Cintrano, C. et al. (2021) ‘Hybridization of Racing Methods with Evolutionary Operators for Simulation Optimization of Traffic Lights Programs’, in Evolutionary Computation in Combinatorial Optimization – 21st European Conference, EvoCOP 2021, Held as Part of EvoStar 2021, Virtual Event, April 7-9, 2021, Proceedings. Springer (Lecture Notes in Computer Science), pp. 17–33. Available at: https://doi.org/10.1007/978-3-030-72904-2_2.
  79. Cintrano, C. et al. (2022) ‘Hybridization of Evolutionary Operators with Elitist Iterated Racing for the Simulation Optimization of Traffic Lights Programs’, Evolutionary Computation, pp. 1–21. Available at: https://doi.org/10.1162/evco_a_00314.
  80. Cintrano, C. and Toutouh, J. (2022) ‘Multiobjective Electric Vehicle Charging Station Locations in a City Scale Area: Malaga Study Case’, in J.L.J. Laredo, J.I. Hidalgo, and K.O. Babaagba (eds) Applications of Evolutionary Computation – 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, Proceedings. Springer (Lecture Notes in Computer Science), pp. 584–600. Available at: https://doi.org/10.1007/978-3-031-02462-7_37.
  81. Cintrano, C., Toutouh, J. and Alba, E. (2021) ‘Citizen Centric Optimal Electric Vehicle Charging Stations Locations in a Full City: Case of Malaga’, in E. Alba et al. (eds) Advances in Artificial Intelligence. Cham: Springer International Publishing, pp. 247–257.
  82. Cintrano, C., Toutouh, J. and Nesmachnow, S. (2021) ‘User-centric multiobjective location of electric vehicle charging stations in a city-scale area’, in 2021 Ivannikov Ispras Open Conference (ISPRAS), pp. 89–95. Available at: https://doi.org/10.1109/ISPRAS53967.2021.00017.
  83. Cipollone, R. et al. (2022) ‘Exploiting Multiple Levels of Abstractions in Episodic RL via Reward Shaping’, in PRL Workshop.
  84. Ciucanu, Radu et al. (2022) ‘SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits’, J. Artif. Intell. Res., 73, pp. 737–765. Available at: https://doi.org/10.1613/jair.1.13163.
  85. Ciucanu, R., Delabrouille, A., et al. (2022) ‘Secure Protocols for Best Arm Identification in Federated Stochastic Multi-Armed Bandits’, IEEE Transactions on Dependable and Secure Computing (TDSC) [Preprint].
  86. Ciucanu, R., Lafourcade, P., et al. (2022) ‘Secure Protocols for Cumulative Reward Maximization in Stochastic Multi-Armed Bandits’, Journal of Computer Security (JCS) [Preprint].
  87. Colnet, A. de and Marquis, P. (2022) ‘On the Complexity of Enumerating Prime Implicants from Decision-DNNF Circuits’, in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022. ijcai.org, pp. 2583–2590.
  88. Console, M. et al. (2021) ‘Intensional and Extensional Views in DL-Lite Ontologies’, in IJCAI.
  89. Console, M., Kolaitis, P.G. and Pieris, A. (2021) ‘Model-theoretic Characterizations of Rule-based Ontologies’, in M. Homola, V. Ryzhikov, and R.A. Schmidt (eds) Proceedings of the 34th International Workshop on Description Logics (DL 2021) part of Bratislava Knowledge September (BAKS 2021), Bratislava, Slovakia, September 19th to 22nd, 2021. CEUR-WS.org (CEUR Workshop Proceedings). Available at: http://ceur-ws.org/Vol-2954/paper-10.pdf.
  90. Cooper, M.C. et al. (2021) ‘A Lightweight Epistemic Logic and its Application to Planning’, Artificial Intelligence, 298, p. 103437. Available at: https://doi.org/10.1016/j.artint.2020.103437.
  91. Coppens, Y. et al. (2020) ‘Synthesising Reinforcement Learning Policies Through Set-Valued Inductive Rule Learning’, in F. Heintz, M. Milano, and B. O’Sullivan (eds) Trustworthy AI – Integrating Learning, Optimization and Reasoning – First International Workshop, TAILOR 2020, Virtual Event, September 4-5, 2020, Revised Selected Papers. Springer (Lecture Notes in Computer Science), pp. 163–179. Available at: https://doi.org/10.1007/978-3-030-73959-1_15.
  92. Corrêa, A.B. et al. (2021) ‘Delete-Relaxation Heuristics for Lifted Classical Planning’, in ICAPS.
  93. Corrêa, A.B. et al. (2022) ‘The FF Heuristic for Lifted Classical Planning’, in AAAI, pp. 9716–9723.
  94. Corrêa, A.B. and Seipp, J. (2022) ‘Best-First Width Search for Lifted Classical Planning’, in ICAPS, pp. 11–15.
  95. Coste-Marquis, S. and Marquis, P. (2021) ‘On Belief Change for Multi-Label Classifier Encodings’, in IJCAI. ijcai.org, pp. 1829–1836.
  96. Couceiro, M. et al. (2020) ‘When Nominal Analogical Proportions Do Not Fail’, in Scalable Uncertainty Management – 14th International Conference, SUM 2020, Bozen-Bolzano, Italy, September 23-25, 2020, Proceedings. Springer (Lecture Notes in Computer Science), pp. 68–83.
  97. Couceiro, M., Haddad, L. and Lagerkvist, V. (2022) ‘A Survey on the Fine-grained Complexity of Constraint Satisfaction Problems Based on Partial Polymorphisms’, J. Multiple Valued Log. Soft Comput., 38(1–2), pp. 115–136.
  98. Couceiro, M. and Lehtonen, E. (2022) ‘A Galois Framework for the Study of Analogical Classifiers’, 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 (IJAI-ECAI 2022)), Vienna, Austria, July 23, 2022, pp. 51–61.
  99. Dahi, Z.A. et al. (2021) ‘A Survey on Quantum Computer Simulators’, in 19th Conference of the Spanish Association for Artificial Intelligence, CAEPIA. CAEPIA, pp. 941–946.
  100. Dahi, Z.A. et al. (2022) ‘Genetic Algorithm for Qubits Initialisation in Noisy Intermediate-Scale Quantum Machines: The IBM Case Study’, in Proceedings of the Genetic and Evolutionary Computation Conference. New York, NY, USA: Association for Computing Machinery (GECCO ’22), pp. 1164–1172. Available at: https://doi.org/10.1145/3512290.3528830.
  101. Dahi, Z.A. and Alba, E. (2022) ‘Metaheuristics on quantum computers: Inspiration, simulation and real execution’, Future Generation Computer Systems, 130, pp. 164–180. Available at: https://doi.org/10.1016/j.future.2021.12.015.
  102. Dahi, Z.A., Alba, E. and Luque, G. (2022) ‘A takeover time-driven adaptive evolutionary algorithm for mobile user tracking in pre-5G cellular networks’, Applied Soft Computing, 116, p. 107992. Available at: https://doi.org/10.1016/j.asoc.2021.107992.
  103. Dahi, Z.A., Luque, G. and Alba, E. (2022) ‘A Machine Learning-Based Approach for Economics-Tailored Applications: The Spanish Case Study’, in Applications of Evolutionary Computation: 25th European Conference, EvoApplications 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings. Berlin, Heidelberg: Springer-Verlag, pp. 567–583. Available at: https://doi.org/10.1007/978-3-031-02462-7_36.
  104. Dahi, Z.A. and Morell, J.Á. (2022) ‘Models and Solvers for Coverage Optimisation in Cellular Networks: Review and Analysis’, in 2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pp. 312–319. Available at: https://doi.org/10.1109/SETIT54465.2022.9875463.
  105. Danassis, P., Erden, Z.D. and Faltings, B. (2022) ‘Exploiting environmental signals to enable policy correlation in large-scale decentralized systems’, Autonomous Agents and Multi-Agent Systems, 36(1), p. 13.
  106. Danassis, P., Triastcyn, A. and Faltings, B. (2022) ‘A Distributed Differentially Private Algorithm for Resource Allocation in Unboundedly Large Settings’, in Proceedings of the 21st International Conference on Autonomous Agents and MultiAgent Systems. (AAMAS ’22).
  107. Daniela De Canditiis, I.D.F. (2021) ‘Anomaly Detection in Multichannel Data Using Sparse Representation in RADWT Frames’, Mathematics, 9(11), p. 1288.
  108. Darwiche, A. and Marquis, P. (2021) ‘On Quantifying Literals in Boolean Logic and Its Applications to Explainable AI’, JAIR, 72, pp. 285–328.
  109. Darwiche, A. and Marquis, P. (2022) ‘On Quantifying Literals in Boolean Logic and its Applications to Explainable AI (Extended Abstract)’, in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022. ijcai.org, pp. 5718–5721.
  110. De Bie, T. et al. (2021) ‘Automating Data Science: Prospects and Challenges’, Communications of the ACM [Preprint].
  111. De Bruijn, H., Warnier, M. and Janssen, M. (2021) ‘The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making’, Government Information Quarterly, 101666, p. Online. Available at: https://doi.org/10.1016/j.giq.2021.101666.
  112. De Filippo, A. et al. (2022) ‘HADA: An automated tool for hardware dimensioning of AI applications’, Knowledge-Based Systems, 251, p. 109199.
  113. De Giacomo, G., Favorito, M., et al. (2022) ‘Modeling resilient cyber-physical processes and their composition from digital twins via Markov Decision Processes’, in. (CEUR).
  114. De Giacomo, G., Felli, P., et al. (2022) ‘Situation Calculus for Controller Synthesis in Manufacturing Systems with First-Order State Representation’, Artificial Intelligence [Preprint].
  115. De Giacomo, G. and Favorito, M. (2021) ‘Compositional Approach to Translate LTLf/LDLf into Deterministic Finite Automata’, in ICAPS.
  116. Delaunay, J., Galárraga, L. and Largouët, C. (2022) ‘When Should We Use Linear Explanations’, in CIKM 2022 – International Conference on Knowledge Management. Atlanta, USA.
  117. Dhami, D.S. et al. (2021) ‘Non-parametric Learning of Embeddings for Relational Data Using Gaifman Locality Theorem’, in Inductive Logic Programming – 30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings. Springer, pp. 95–110.
  118. Dhami, D.S., Das, M. and Natarajan, S. (2021) ‘Beyond Simple Images: Human Knowledge-Guided GANs for Clinical Data Generation’, in Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning, KR 2021, Online event, November 3-12, 2021, pp. 247–257.
  119. Di Liello, L. et al. (2020) ‘Efficient Generation of Structured Objects with Constrained Adversarial Networks’, Advances in Neural Information Processing Systems, 33.
  120. Díaz-Munío, G.V.G. et al. (2021) ‘Europarl-ASR: A Large Corpus of Parliamentary Debates for Streaming ASR Benchmarking and Speech Data Filtering/Verbatimization’, in Interspeech. ISCA.
  121. Domínguez-Ríos, M.Á., Chicano, F. and Alba, E. (2021a) ‘Effective anytime algorithm for multiobjective combinatorial optimization problems’, Information Sciences, 565, pp. 210–228. Available at: https://doi.org/10.1016/j.ins.2021.02.074.
  122. Domínguez-Ríos, M.Á., Chicano, F. and Alba, E. (2021b) ‘Improving Search Efficiency and Diversity of Solutions in Multiobjective Binary Optimization by Using Metaheuristics Plus Integer Linear Programming’, in Applications of Evolutionary Computation. Cham: Springer International Publishing, pp. 242–257.
  123. Dominik Drexler, J.S. and Speck, D. (2021) ‘Subset-Saturated Transition Cost Partitioning’, in ICAPS.
  124. Donadello, I. et al. (2022) ‘Machine Learning for Utility Prediction in Argument-Based Computational Persuasion’, in AAAI. AAAI Press.
  125. Doolaard, F. and Yorke-Smith, N. (2022) ‘Online Learning of Variable Ordering Heuristics for Constraint Optimisation Problems’, Annals of Mathematics and Artificial Intelligence [Preprint].
  126. Drexler, D., Seipp, J. and Geffner, H. (2021) ‘Expressing and Exploiting the Common Subgoal Structure of Classical Planning Domains Using Sketches’, in KR.
  127. Drexler, D., Seipp, J. and Geffner, H. (2022) ‘Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width’, in ICAPS.
  128. Driel, R. van, Demirovic, E. and Yorke-Smith, N. (2021) ‘Learning Variable Activity Initialisation for Lazy Clause Generation Solvers’, in Integration of Constraint Programming, Artificial Intelligence, and Operations Research – 18th International Conference, CPAIOR 2021, Vienna, Austria, July 5-8, 2021, Proceedings. Springer (Lecture Notes in Computer Science), pp. 62–71. Available at: https://doi.org/10.1007/978-3-030-78230-6_4.
  129. Eggensperger, K. et al. (2021) ‘HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO’, in Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).
  130. El Baz, A., Guyon, I., Liu, Z., van Rijn, Jan N., et al. (2021) ‘Advances in MetaDL: AAAI 2021 challenge and workshop’, Proceedings of Machine Learning Research [Preprint]. Available at: https://proceedings.mlr.press/v140/el-baz21a.html.
  131. El Baz, A., Guyon, I., Liu, Z., van Rijn, Jan Nicolaas, et al. (2021) ‘MetaDL challenge design and baseline results’, in I. Guyon et al. (eds) AAAI Workshop on Meta-Learning and MetaDL Challenge. PMLR (Proceedings of Machine Learning Research), pp. 1–16.
  132. Errica, F. et al. (2021) ‘Robust Malware Classification via Deep Graph Networks on Call Graph Topologies’, in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
  133. Errica, F., Bacciu, D. and Micheli, A. (2021a) ‘Graph Mixture Density Networks’, in Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event. PMLR (Proceedings of Machine Learning Research), pp. 3025–3035.
  134. Errica, F., Bacciu, D. and Micheli, A. (2021b) ‘Graph Mixture Density Networks’, in Proceedings of the 38th International Conference on Machine Learning. PMLR (Proceedings of Machine Learning Research), pp. 3025–3035.
  135. Esteban, M., Toutouh, J. and Nesmachnow, S. (2021) ‘Parallel/Distributed Intelligent Hyperparameters Search for Generative Artificial Neural Networks’, in H. Jagode et al. (eds) High Performance Computing. Cham: Springer International Publishing, pp. 297–313. Available at: https://doi.org/10.1007/978-3-030-90539-2_20.
  136. Ferber, P., Cohen, L., et al. (2022) ‘Learning and Exploiting Progress States in Greedy Best-First Search’, in IJCAI, pp. 4740–4746.
  137. Ferber, P., Geißer, F., et al. (2022) ‘Neural Network Heuristic Functions for Classical Planning: Bootstrapping and Comparison to Other Methods’, in ICAPS, pp. 583–587.
  138. Fisher, M. et al. (2021) ‘Towards a framework for certification of reliable autonomous systems’, Auton. Agents Multi Agent Syst., 35(1), p. 8. Available at: https://doi.org/10.1007/s10458-020-09487-2.
  139. Flores, D. et al. (2022) ‘Coevolutionary generative adversarial networks for medical image augumentation at scale’, in J.E. Fieldsend and M. Wagner (eds) GECCO ’22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9 – 13, 2022. ACM, pp. 367–376. Available at: https://doi.org/10.1145/3512290.3528742.
  140. Fontana, M., Naretto, F. and Monreale, A. (2021) ‘A new approach for cross-silo federated learning and its privacy risks’, in 18th International Conference on Privacy, Security and Trust, PST 2021, Auckland, New Zealand, December 13-15, 2021. IEEE, pp. 1–10. Available at: https://doi.org/10.1109/PST52912.2021.9647753.
  141. Fraga Pereira, R., Fuggitti, F. and De Giacomo, G. (2021) ‘Recognizing LTLf/PLTLf Goals in Fully Observable Non-Deterministic Domain Models’, CoRR, abs/2103.11692.
  142. Francès, G., Bonet, B. and Geffner, H. (2021) ‘Learning General Planning Policies from Small Examples Without Supervision’, in AAAI, pp. 11801–11808.
  143. Gabriele Ciravegna, M.M., Pietro Barbiero, Francesco Giannini, Marco Gori, Pietro Liò and Melacci, S. (2021) ‘Logic Explained Networks’, CoRR, abs/2108.05149.
  144. Galindo, C., Pérez, S. and Silva, J. (2023) ‘Program slicing of Java programs’, Journal of Logical and Algebraic Methods in Programming, 2023(1). Available at: https://doi.org/10.1016/j.jlamp.2022.100826.
  145. Garcia-Piqueras, M. and Hernández-Orallo, J. (2021) ‘Optimal Teaching Curricula with Compositional Simplicity Priors’, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, pp. 705–721.
  146. Gaudel, R. et al. (2022) ‘s-LIME: Reconciling Locality and Fidelity in Linear Explanations’, in IDA 2022 – Symposium on Intelligent Data Analysis. Rennes, France, pp. 1–13. Available at: https://hal.inria.fr/hal-03741042.
  147. Geffner, H. (2022) ‘Target Languages (vs. Inductive Biases) for Learning to Act and Plan’, in AAAI. AAAI Press, pp. 12326–12333.
  148. Geisler, S. et al. (2022) ‘Knowledge-Driven Data Ecosystems Toward Data Transparency’, ACM J. Data Inf. Qual., 14(1), p. 3:1-3:12. Available at: https://doi.org/10.1145/3467022.
  149. Georgara, A. et al. (2022) ‘An Anytime Heuristic Algorithm for Allocating Many Teams to Many Tasks’, in Proceedings of the 21st International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, pp. 1598–1600.
  150. Georgara, A., Rodríguez-Aguilar, J.A. and Sierra, C. (2021) ‘Towards a Competence-Based Approach to Allocate Teams to Tasks’, in Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, pp. 1504–1506.
  151. Georgara, A., Rodríguez-Aguilar, J.A. and Sierra, C. (2022a) ‘Allocating teams to tasks: an anytime heuristic competence-based approach’, in Proceedings of the 9th European Conference on MultiAgent Systems.
  152. Georgara, A., Rodríguez-Aguilar, J.A. and Sierra, C. (2022b) ‘Building Contrastive Explanations for Multi-agent Team Formation’, in Proceedings of the 21st International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, pp. 516–524.
  153. Georgara, A., Rodríguez-Aguilar, J.A. and Sierra, C. (2022c) ‘Privacy-Aware Explanations for Team Formation’, in Proceedings of the 24th International Conference on Principles and Practice of Multi-Agent Systems.
  154. Giacomo, G.D., Favorito, M., Iocchi, L., et al. (2021) ‘Domain-Independent Reward Machines for Modular Integration of Planning and Learning’, in PRL Workshop.
  155. Giacomo, G.D., Stasio, A.D., Tabajara, L.M., et al. (2021) ‘Finite-Trace and Generalized-Reactivity Specifications in Temporal Synthesis’, in IJCAI.
  156. Giacomo, G.D., Felli, P., et al. (2021) ‘HyperLDLf: a Logic for Checking Properties of Finite Traces Process Logs’, in IJCAI.
  157. Giacomo, G.D., Oriol, X., et al. (2021) ‘Instance-Level Update in DL-Lite Ontologies through First-Order Rewriting’, JAIR, 70, pp. 1335–1371. Available at: https://doi.org/10.1613/jair.1.12414.
  158. Giacomo, G.D., Stasio, A.D., Perelli, G., et al. (2021) ‘Synthesis with Mandatory Stop Actions’, in KR.
  159. Giacomo, G.D., Murano, A., Patrizi, F., et al. (2021) ‘Timed Trace Alignment with Metric Temporal Logic over Finite Traces’, in KR.
  160. Giacomo, G.D., Favorito, M., Mecella, M., et al. (2022) ‘Digital Twins Composition via Markov Decision Processes’, in ITAL-IA Workshop.
  161. Giacomo, G.D., Favorito, M., Li, J., et al. (2022) ‘LTLf Synthesis as AND-OR Graph Search: Knowledge Compilation at Work’, in IJCAI. ijcai.org, pp. 2591–2598.
  162. Giacomo, G.D., Favorito, M. and Fuggitti, F. (2022) ‘Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic: A Polynomial Reduction to Standard Planning’, CoRR, abs/2204.09960.
  163. Giacomo, G.D. and Lespérance, Y. (2021) ‘The Nondeterministic Situation Calculus’, in KR.
  164. Giacomo, G.D. and Perelli, G. (2021) ‘Behavioral QLTL’, CoRR, abs/2102.11184.
  165. Giunchiglia, F., Erculiani, L. and Passerini, A. (2021) ‘Towards Visual Semantics’, SN COMPUT. SCI., 2(446). Available at: https://doi.org/10.1007/s42979-021-00839-7.
  166. Gretkowski, A., Wisniewski, D. and Lawrynowicz, A. (2022) ‘Should We Afford Affordances? Injecting ConceptNet Knowledge into BERT-Based Models to Improve Commonsense Reasoning Ability’, in Knowledge Engineering and Knowledge Management – 23rd International Conference, EKAW 2022, Bolzano, Italy, September 26-29, 2022, Proceedings. Springer (Lecture Notes in Computer Science), pp. 97–104. Available at: https://doi.org/10.1007/978-3-031-17105-5_7.
  167. Guidotti, R. et al. (2020) ‘Explaining Any Time Series Classifier’, in 2nd IEEE International Conference on Cognitive Machine Intelligence, CogMI 2020, Atlanta, GA, USA, October 28-31, 2020. IEEE, pp. 167–176. Available at: https://doi.org/10.1109/CogMI50398.2020.00029.
  168. Guidotti, R. (2021) ‘Evaluating local explanation methods on ground truth’, Artif. Intell., 291, p. 103428. Available at: https://doi.org/10.1016/j.artint.2020.103428.
  169. Guidotti, R. et al. (2021) ‘Principles of Explainable Artificial Intelligence’, in Explainable AI Within the Digital Transformation and Cyber Physical Systems. Springer, pp. 9–31.
  170. Guidotti, R. and D’Onofrio, M. (2021) ‘Matrix Profile-Based Interpretable Time Series Classifier’, Frontiers Artif. Intell., 4, p. 699448. Available at: https://doi.org/10.3389/frai.2021.699448.
  171. Guidotti, R. and Monreale, A. (2021) ‘Designing Shapelets for Interpretable Data-Agnostic Classification’, in AIES ’21: AAAI/ACM Conference on AI, Ethics, and Society, Virtual Event, USA, May 19-21, 2021. ACM, pp. 532–542. Available at: https://doi.org/10.1145/3461702.3462553.
  172. Guidotti, R. and Ruggieri, S. (2021) ‘Ensemble of Counterfactual Explainers’, in DS. Springer (Lecture Notes in Computer Science), pp. 358–368.
  173. Guidotti, R. and Viotto, S. (2020) ‘Interpretable Next Basket Prediction Boosted with Representative Recipes’, in 2nd IEEE International Conference on Cognitive Machine Intelligence, CogMI 2020, Atlanta, GA, USA, October 28-31, 2020. IEEE, pp. 62–71. Available at: https://doi.org/10.1109/CogMI50398.2020.00018.
  174. Gusmao, K.M. et al. (2021) ‘Inferring Agents Preferences as Priors for Probabilistic Goal Recognition’, CoRR, abs/2102.11791.
  175. Gutierrez, J., Murano, A., et al. (2021) ‘Equilibria for games with combined qualitative and quantitative objectives’, Acta Informatica, 58(6), pp. 585–610.
  176. Gutierrez, J., Harrenstein, P., et al. (2021) ‘Expressiveness and Nash Equilibrium in Iterated Boolean Games’, ACM Trans. Comput. Logic, 22(2). Available at: https://doi.org/10.1145/3439900.
  177. Habering, D., Hofmann, T. and Lakemeyer, G. (2021) ‘Using Platform Models for a Guided Explanatory Diagnosis Generation for Mobile Robots’, in IJCAI. ijcai.org, pp. 1908–1914.
  178. Haoran Peng, T.L., He Huang, Li Xu, Tianjiao Li, Jun Liu, Hossein Rahmani, Qiuhong Ke, Zhicheng Guo, Cong Wu, Rongchang Li, Mang Ye, Jiahao Wang, Jiaxu Zhang, Yuanzhong Liu, Tao He, Fuwei Zhang, Xianbin Liu (2021) ‘The Multi-Modal Video Reasoning and Analyzing Competition’, in International Conference on Computer Vision- Workshop.
  179. Haoxuan Qu, B.W., Hossein Rahmani, Li Xu and Liu, J. (2021) ‘Recent Advances of Continual Learning in Computer Vision: An Overview’, CoRR, abs/2109.11369.
  180. Harada, T., Alba, E. and Luque, G. (2022) ‘A fresh approach to evaluate performance in distributed parallel genetic algorithms’, Applied Soft Computing, 119, p. 108540. Available at: https://doi.org/10.1016/j.asoc.2022.108540.
  181. Heller, D. et al. (2022) ‘Neural Network Heuristic Functions: Taking Confidence into Account’, in SoCS, pp. 223–228.
  182. Helmert, M. et al. (2022) ‘On the Complexity of Heuristic Synthesis for Satisficing Classical Planning: Potential Heuristics and Beyond’, in ICAPS, pp. 124–133.
  183. Herzig, A., Lorini, E. and Perrotin, E. (2022) ‘A Computationally Grounded Logic of “Seeing-to-it-that”’, in L.D. Raedt (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022. ijcai.org, pp. 2648–2654. Available at: https://doi.org/10.24963/ijcai.2022/367.
  184. Herzig, A., Maris, F. and Perrotin, E. (2021) ‘A Dynamic Epistemic Logic with Finite Iteration and Parallel Composition’, in Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning, KR 2021, Online event, November 3-12, 2021, pp. 676–680. Available at: https://doi.org/10.24963/kr.2021/68.
  185. Herzig, A. and Yuste-Ginel, A. (2021a) ‘Abstract Argumentation with Qualitative Uncertainty: An Analysis in Dynamic Logic’, in Logic and Argumentation – 4th International Conference, CLAR 2021, Hangzhou, China, October 20-22, 2021, Proceedings. Springer (Lecture Notes in Computer Science), pp. 190–208. Available at: https://doi.org/10.1007/978-3-030-89391-0_11.
  186. Herzig, A. and Yuste-Ginel, A. (2021b) ‘Multi-Agent Abstract Argumentation Frameworks With Incomplete Knowledge of Attacks’, in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021. ijcai.org, pp. 1922–1928. Available at: https://doi.org/10.24963/ijcai.2021/265.
  187. Herzig, A. and Yuste-Ginel, A. (2021c) ‘On the Epistemic Logic of Incomplete Argumentation Frameworks’, in Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning, KR 2021, Online event, November 3-12, 2021, pp. 681–685. Available at: https://doi.org/10.24963/kr.2021/69.
  188. Hlávková, Z. et al. (2022) ‘An API for DL Abduction Solvers’, in Description Logics. CEUR-WS.org (CEUR Workshop Proceedings).
  189. Homola, M. et al. (2020) ‘Merge, Explain, Iterate’, in 33rd Int’l Workshop on Description Logics (DL 2020). CEUR-WS.org, pp. 1–11.
  190. Homola, M. et al. (2022) ‘Hybrid MHS-MXP ABox Abduction Solver: First Empirical Results’, in Description Logics. CEUR-WS.org (CEUR Workshop Proceedings).
  191. Hutiri, W.T. and Ding, A.Y. (2022) ‘Bias in Automated Speaker Recognition’, in Proceedings of ACM FAccT 2022. ​​Seoul, Republic of Korea.
  192. Hvarfner, C. et al. (2022) ‘Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization’, in International Conference on Learning Representations.
  193. Iranzo-Sánchez, J., Jorge, J., Baquero-Arnal, P., Silvestre-Cerdà, J.A., Giménez, A., Civera, J., Sanchís, A., et al. (2021) ‘Streaming cascade-based speech translation leveraged by a direct segmentation model’, Neural Networks, 142, pp. 303–315.
  194. Iranzo-Sánchez, J., Jorge, J., Baquero-Arnal, P., Silvestre-Cerdà, J.A., Giménez, A., Civera, J., Sanchis, A., et al. (2021) ‘Streaming cascade-based speech translation leveraged by a direct segmentation model’, Neural Networks, 142, pp. 303–315. Available at: https://doi.org/10.1016/j.neunet.2021.05.013.
  195. Iranzo-Sánchez, J., Civera, J. and Juan, A. (2022) ‘From Simultaneous to Streaming Machine Translation by Leveraging Streaming History’, in Proc. 60th Annual Meeting of the Association for Computational Linguistics Vol. 1: Long Papers (ACL 2022). Dublin (Ireland), pp. 6972–6985. Available at: https://doi.org/10.18653/v1/2022.acl-long.480.
  196. Javier Iranzo-Sánchez, A.J., Jorge Civera (2021) ‘Stream-level Latency Evaluation for Simultaneous Machine Translation’, in Findings of the ACL: EMNLP 2021. Punta Cana (Dominican Republic), pp. 664–670. Available at: https://doi.org/10.18653/v1/2021.findings-emnlp.58.
  197. Jia Gong, J.L., Zhipeng Fan, Qiuhong Ke, Hossein Rahmani (2022) ‘Meta Agent Teaming Active Learning for Pose Estimation’, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  198. Jianhong Pan, J.L., Qichen Zheng, Zhipeng Fan, Hossein Rahmani, Qiuhong Ke (2022) ‘GradAuto: Energy-oriented Attack on Dynamic Neural Networks’, in European Conference on Computer Vision.
  199. Jorge, J. et al. (2021) ‘Live Streaming Speech Recognition Using Deep Bidirectional LSTM Acoustic Models and Interpolated Language Models’, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30, pp. 148–161. Available at: https://doi.org/10.1109/TASLP.2021.3133216.
  200. Kicki, P. et al. (2021) ‘Learning from experience for rapid generation of local car maneuvers’, Engineering Applications of Artificial Intelligence, 105, p. 104399. Available at: https://doi.org/10.1016/j.engappai.2021.104399.
  201. Kicki, P., Skrzypczy\’nski, P. and Ozay, M. (2021) ‘A New Approach to Design Symmetry Invariant Neural Networks’, in IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1–8.
  202. Kim, Y., Allmendinger, R. and López-Ibáñez, M. (2020) ‘Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art’, in Trustworthy AI – Integrating Learning, Optimization and Reasoning – First International Workshop, TAILOR 2020, Virtual Event, September 4-5, 2020, Revised Selected Papers. Springer (Lecture Notes in Computer Science), pp. 123–139. Available at: https://doi.org/10.1007/978-3-030-73959-1_12.
  203. Kin Max Gusmão, F.M., Ramon Fraga Pereira (2021) ‘Inferring Agents Preferences as Priors for Probabilistic Goal Recognition’, in SPARK Workshop (ICAPS).
  204. Kloska, M. and Rozinajová, V. (2021) ‘Towards Symbolic Time Series Representation Improved by Kernel Density Estimators’, Trans. Large Scale Data Knowl. Centered Syst., 50, pp. 25–45.
  205. Klößner, T. et al. (2022) ‘Cost Partitioning Heuristics for Stochastic Shortest Path Problems’, in ICAPS, pp. 193–202.
  206. Kompan, M. et al. (2021) ‘Exploring Customer Price Preference and Product Profit Role in Recommender Systems’, in IEEE Intelligent Systems. IEEE, pp. 89–98.
  207. König, M., Hoos, H.H. and van Rijn, J.N. (2021) ‘Speeding Up Neural Network Verification via Automated Algorithm Configuration’, in ICLR workshop: Security and Safety in Machine Learning Systems.
  208. König, M., Hoos, H.H. and van Rijn, J.N. (2022) ‘Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio’, Machine Learning, 111(9).
  209. Kostovska, A. et al. (2021) ‘OPTION: optimization algorithm benchmarking ontology’, in GECCO ’21: Genetic and Evolutionary Computation Conference, Companion Volume, Lille, France, July 10-14, 2021. ACM, pp. 239–240. Available at: https://doi.org/10.1145/3449726.3459579.
  210. Kostovska, A. et al. (2022) ‘A catalogue with semantic annotations makes multilabel datasets FAIR’, Scientific Reports, 12(7267), pp. 1–11. Available at: https://doi.org/10.1038/s41598-022-11316-3.
  211. Krawiec, K. and Kossinski, D. (2022) ‘Compositional genetic programming for symbolic regression’, in J.E. Fieldsend and M. Wagner (eds) GECCO ’22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9 – 13, 2022. ACM, pp. 570–573. Available at: https://doi.org/10.1145/3520304.3529077.
  212. Kumar, M. et al. (2021) ‘Learning Mixed-Integer Linear Programs from Contextual Examples’, Data Science Meets Optimization Workshop [Preprint].
  213. Lamanna, L., Saetti, A., et al. (2021) ‘Online Learning of Action Models for PDDL Planning’, in IJCAI.
  214. Lamanna, L., Serafini, L., et al. (2021) ‘On-line Learning of Planning Domains from Sensor Data in PAL: Scaling up to Large State Spaces’, in AAAI.
  215. Lamanna, L. et al. (2022) ‘Online Grounding of Symbolic Planning Domains in Unknown Environments’, in KR.
  216. Lango, M. and Stefanowski, J. (2022) ‘What makes multi-class imbalanced problems difficult? An experimental study’, Expert Systems with Applications, 199, p. 116962.
  217. Latour, A.L.D. et al. (2022) ‘Exact stochastic constraint optimisation with applications in network analysis’, Artif. Intell., 304, p. 103650.
  218. Lenzerini, M., Lepore, L. and Poggi, A. (2021) ‘Metamodeling and metaquerying in OWL 2 QL’, Artificial Intelligence, 292, p. 103432. Available at: https://doi.org/10.1016/j.artint.2020.103432.
  219. Liu, D. and Lakemeyer, G. (2021) ‘Reasoning about Beliefs and Meta-Beliefs by Regression in an Expressive Probabilistic Action Logic’, in IJCAI. ijcai.org, pp. 1951–1958.
  220. Lombardi, M. et al. (2020) ‘An analysis of regularized approaches for constrained machine learning’, in International Workshop on the Foundations of Trustworthy AI Integrating Learning, Optimization and Reasoning. Springer, pp. 112–119.
  221. Ma, X. et al. (2022) ‘REMOTE: Reinforced Motion Transformation Network for Semi-Supervised 2D Pose Estimation in Videos’, in AAAI.
  222. Maaroof, N. et al. (2022) ‘A Comparative Study of Two Rule-Based Explanation Methods for Diabetic Retinopathy Risk Assessment’, Applied Sciences, 12(7). Available at: https://doi.org/10.3390/app12073358.
  223. Machín, B., Nesmachnow, S. and Toutouh, J. (2022) ‘Multi-target evolutionary latent space search of a generative adversarial network for human face generation’, in J.E. Fieldsend and M. Wagner (eds) GECCO ’22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9 – 13, 2022. ACM, pp. 1878–1886. Available at: https://doi.org/10.1145/3520304.3533992.
  224. Maciej Falbogowski, A.W., Jerzy Stefanowski, Zuzanna Trafas (2022) ‘The impact of using constraints on counterfactual explanations’, in Proceedings of the 3rd Polish Conference on Artificial Intelligence PP-RAI, April 25-27, 2022, Gdynia, Poland. Gdynia Maritime University, pp. 81–84. Available at: https://wydawnictwo.umg.edu.pl/pp-rai2022/pdfs/19_pp-rai-2022-028.pdf.
  225. Magalhaes, C., Araujo, J. and Sardinha, A. (2021) ‘MARE: an Active Learning Approach for Requirements Classification’, in 2021 IEEE 29th International Requirements Engineering Conference (RE). Los Alamitos, CA, USA: IEEE Computer Society, pp. 516–521. Available at: https://doi.org/10.1109/RE51729.2021.9714537.
  226. Maouche, M. et al. (2022) ‘Enhancing speech privacy with slicing’, in Interspeech 2022.
  227. Marco Alberti, F.R., Riccardo Zese and Lamma, E. (2022) ‘An Iterative Fixpoint Semantics for MKNF Hybrid Knowledge Bases with Function Symbols’, CoRR, abs/2208.03092.
  228. Mariani, G., Monreale, A. and Naretto, F. (2021) ‘Privacy Risk Assessment of Individual Psychometric Profiles’, in Discovery Science – 24th International Conference, DS 2021, Halifax, NS, Canada, October 11-13, 2021, Proceedings. Springer (Lecture Notes in Computer Science), pp. 411–421. Available at: https://doi.org/10.1007/978-3-030-88942-5_32.
  229. Marquer, E. et al. (2022) ‘A Deep Learning Approach to Solving Morphological Analogies’, in Case-Based Reasoning Research and Development – 30th International Conference, ICCBR 2022, Nancy, France, September 12-15, 2022, Proceedings, pp. 159–174.
  230. Marquer, E., Murena, P.-A. and Couceiro, M. (2022) ‘Transferring Learned Models of Morphological Analogy’, in ATA@ICCBR2022 – Analogies: from Theory to Applications (ATA@ICCBR2022). Nancy, France. Available at: https://hal.inria.fr/hal-03783959.
  231. Marra, G. et al. (2021) ‘From Statistical Relational to Neural Symbolic Artificial Intelligence: a Survey’, CoRR, abs/2108.11451. Available at: https://arxiv.org/abs/2108.11451.
  232. Martínez-Plumed, F. et al. (2021) ‘Research community dynamics behind popular AI benchmarks’, Nature Machine Intelligence, 3(7), pp. 581–589.
  233. Martinez-Vaquero, L.A., Santos, F.C. and Trianni, V. (2021) ‘Signalling boosts the evolution of cooperation in repeated group interactions’, Journal of the Royal Society Interface, 17(172), p. 20200635. Available at: https://doi.org/10.1098/rsif.2020.0635.
  234. Mateo Espinosa Zarlenga, P.L., Pietro Barbiero, Gabriele Ciravegna, Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Zohreh Shams, Frédéric Precioso, Stefano Melacci, Adrian Weller and Jamnik, M. (2022) ‘Concept Embedding Models’, CoRR, abs/2209.09056.
  235. Mehta, Y. et al. (2022) ‘NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy’, in International Conference on Learning Representations.
  236. Meneguzzi, F. and Pereira, R.F. (2021) ‘A Survey on Goal Recognition as Planning’, in IJCAI.
  237. Menguy, G. et al. (2022) ‘Automated Program Analysis: Revisiting Precondition Inference through Constraint Acquisition’, in IJCAI. Available at: https://doi.org/10.24963/ijcai.2022/260.
  238. Metta, C. et al. (2021) ‘Exemplars and Counterexemplars Explanations for Image Classifiers, Targeting Skin Lesion Labeling’, in IEEE Symposium on Computers and Communications, ISCC 2021, Athens, Greece, September 5-8, 2021. IEEE, pp. 1–7. Available at: https://doi.org/10.1109/ISCC53001.2021.9631485.
  239. Michelangelo Diligenti, M.M., Francesco Giannini, Marco Gori and Marra, G. (2021) ‘A Constraint-Based Approach to Learning and Reasoning’, in P. Hitzler and M.K. Sarker (eds) Neuro-Symbolic Artificial Intelligence: The State of the Art. IOS Press (Frontiers in Artificial Intelligence and Applications), pp. 192–213.
  240. Micheli, A. and Valentini, A. (2021) ‘Synthesis of Search Heuristics for Temporal Planning via Reinforcement Learning’, in AAAI.
  241. Misino, E., Marra, G. and Sansone, E. (2022) ‘VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming’, in Neural Information Processing Systems (NeurIPS).
  242. Mittelmann, M. et al. (2022a) ‘Automated Synthesis of Mechanisms’, in IJCAI. ijcai.org, pp. 426–432.
  243. Mittelmann, M. et al. (2022b) ‘Synthesis of Mechanisms with Strategy Logic (short paper)’, in ICTCS. CEUR-WS.org (CEUR Workshop Proceedings).
  244. Mittelmann, M., Herzig, A. and Perrussel, L. (2021) ‘Epistemic Reasoning About Rationality and Bids in Auctions’, in Logics in Artificial Intelligence – 17th European Conference, JELIA 2021, Virtual Event, May 17-20, 2021, Proceedings. Springer (Lecture Notes in Computer Science), pp. 116–130. Available at: https://doi.org/10.1007/978-3-030-75775-5_9.
  245. Montes, N., Osman, N. and Sierra, C. (2021) ‘Enabling Game-Theoretical Analysis of Social Rules’, in CCIA. IOS Press (Frontiers in Artificial Intelligence and Applications), pp. 90–99.
  246. Montes, N., Osman, N. and Sierra, C. (2022) ‘A computational model of Ostrom’s Institutional Analysis and Development framework’, Artificial Intelligence, 311, p. 103756. Available at: https://doi.org/10.1016/j.artint.2022.103756.
  247. Montes, N. and Sierra, C. (2020) ‘Value-Alignment Equilibrium in Multiagent Systems’, in TAILOR. Springer (Lecture Notes in Computer Science), pp. 189–204. Available at: https://doi.org/10.1007/978-3-030-73959-1_17.
  248. Montes, N. and Sierra, C. (2021) ‘Value-Guided Synthesis of Parametric Normative Systems’, in AAMAS. ACM, pp. 907–915.
  249. Montes, N. and Sierra, C. (2022) ‘Synthesis and Properties of Optimally Value-Aligned Normative Systems’, Journal of Artificial Intelligence Research, 74, pp. 1739–1774. Available at: https://doi.org/10.1613/jair.1.13487.
  250. Morell, J.Á. et al. (2022) ‘Optimising Communication Overhead in Federated Learning Using NSGA-II”, booktitle=”Applications of Evolutionary Computation’, in J.L. Jiménez Laredo, J.I. Hidalgo, and K.O. Babaagba (eds). Cham: Springer International Publishing, pp. 317–333.
  251. Morell, J.Á. and Alba, E. (2022) ‘Dynamic and adaptive fault-tolerant asynchronous federated learning using volunteer edge devices’, Future Generation Computer Systems, 133, pp. 53–67. Available at: https://doi.org/10.1016/j.future.2022.02.024.
  252. Morettin, P. et al. (2021) ‘Hybrid probabilistic inference with logical and algebraic constraints: a survey’, in Proceedings of the 30th International Joint Conference on Artificial Intelligence.
  253. Morettin, P., Passerini, A. and Teso, S. (2021) ‘Co-creating Platformer Levels with Constrained Adversarial Networks’, Workshop on Human-AI Co-Creation with Generative Models at IUI 2021 [Preprint].
  254. Nadi, A. et al. (2022) ‘A Data-driven Time-Dependent Routing and Scheduling for Activity-Based Freight Transport Modelling’, in Proceedings of the 11th Triennial Symposium on Transportation Analysis (TRISTAN XI 2022). Mauritius.
  255. Neves, A. and Sardinha, A. (2022) ‘Learning to Cooperate with Completely Unknown Teammates’, in Progress in Artificial Intelligence. Cham: Springer International Publishing.
  256. Norel, M. et al. (2021) ‘Climate Variability Indices—A Guided Tour’, Geosciences, 11(3). Available at: https://doi.org/10.3390/geosciences11030128.
  257. Norel, M., Krawiec, K. and Kundzewicz, Z.W. (2021) ‘Machine Learning Modeling of Climate Variability Impact on River Runoff’, Water, 13(9). Available at: https://doi.org/10.3390/w13091177.
  258. Nowak, T. and Skrzypczyński, P. (2022) ‘Geometry-Aware Keypoint Network: Accurate Prediction of Point Features in Challenging Scenario’, in 17th Conference on Computer Science and Intelligence Systems (FedCSIS), pp. 191–200. Available at: https://doi.org/10.15439/2022F145.
  259. Numeroso, D. and Bacciu, D. (2021a) ‘MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks’, CoRR, abs/2104.08060.
  260. Numeroso, D. and Bacciu, D. (2021b) ‘MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks’, CoRR, abs/2104.08060. Available at: https://arxiv.org/abs/2104.08060.
  261. Oriol, X. et al. (2021) ‘Embedding Reactive Behavior into Artifact-centric Business Process Models’, FGCS, 117, pp. 97–110. Available at: https://doi.org/10.1016/j.future.2020.11.018.
  262. Ottervanger, G., Baratchi, M. and Hoos, H.H. (2021) ‘MultiETSC: automated machine learning for early time series classification’, Data Min. Knowl. Discov., 35(6), pp. 2602–2654.
  263. Oudshoorn, M., Koppenberg, T. and Yorke-Smith, N. (2021) ‘Optimisation of Annual Planned Rail Maintenance’, Computer-Aided Civil and Infrastructure Engineering, e-print ahead of press.
  264. Overwater, A. and Yorke-Smith, N. (2022) ‘Agent-Based Simulation of Short-Term Peer-to-Peer Rentals: Evidence from the Amsterdam Housing Market’, Environment and Planning B: Urban Analytics and City Science, 49(1), pp. 223–240.
  265. Panigutti, C. et al. (2021) ‘FairLens: Auditing black-box clinical decision support systems’, Information Processing & Management, 58(5), p. 102657.
  266. Papadopoulos, C. et al. (2022) ‘Efficient Learning of Multiple NLP Tasks via Collective Weight Factorization on BERT’, in NAACL-HLT (Findings). Association for Computational Linguistics, pp. 882–890.
  267. Parr, T. and Pezzulo, G. (2021) ‘Understanding, Explanation, and Active Inference’, Frontiers in Systems Neuroscience, 15.
  268. Paterson-Jones, G., Casini, G. and Meyer, T. (2021) ‘A Boolean Extension of KLM-Style Conditional Reasoning’, in Southern African Conference for Artificial Intelligence Research. Springer, pp. 236–252.
  269. Patrick Ferber, J.S. (2022) ‘Explainable Planner Selection for Classical Planning’, in AAAI. AAAI Press.
  270. Pavlík, P., Rozinajová, V. and Ezzeddine, A.B. (2022) ‘Radar-Based Volumetric Precipitation Nowcasting: A 3D Convolutional Neural Network with U-Net Architecture’, in Proceedings of the Second Workshop on Complex Data Challenges in Earth Observation (CDCEO 2022). CEUR-WS.org, p. 8.
  271. Pellegrini, G. et al. (2021) ‘Learning Aggregation Functions’, in Proceedings of the 30th International Joint Conference on Artificial Intelligence.
  272. Pereira, R.F., Oren, N. and Meneguzzi, F. (2021) ‘Landmark-Based Approaches for Goal Recognition as Planning’, in ICAPS, Journal Track.
  273. Petkovic, M. et al. (2021) ‘Ensemble- and distance-based feature ranking for unsupervised learning’, Int. J. Intell. Syst., 36(7), pp. 3068–3086. Available at: https://doi.org/10.1002/int.22390.
  274. Petkovic, M. et al. (2022) ‘Relational tree ensembles and feature rankings’, Knowl. Based Syst., 251, p. 109254. Available at: https://doi.org/10.1016/j.knosys.2022.109254.
  275. Petkovic, M., Dzeroski, S. and Kocev, D. (2022) ‘Feature ranking for semi-supervised learning’, Mach. Learn. [Preprint]. Available at: https://doi.org/10.1007/s10994-022-06181-0.
  276. Pietro Barbiero, M.G., Gabriele Ciravegna, Francesco Giannini, Pietro Liò and Melacci, S. (2022) ‘Entropy-Based Logic Explanations of Neural Networks’, 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, pp. 6046–6054.
  277. Pingen, G.L.J. et al. (2022) ‘Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics’, in Proceedings of the 32nd International Conference on Automated Planning and Scheduling (ICAPS’22). Singapore (virtual).
  278. Pluciński, K. and Klimczak, H. (2021) ‘GHOST at SemEval-2021 Task 5: Is explanation all you need?’, in Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021). Association for Computational Linguistics, pp. 852–859. Available at: https://doi.org/10.18653/v1/2021.semeval-1.114.
  279. Pócoš, Š. et al. (2021) ‘Assessment of Manifold Unfolding in Trained Deep Neural Network Classifiers’, in Trustworthy AI – Integrating Learning, Optimization and Reasoning. Springer International Publishing, pp. 93–103.
  280. Pócoš, Š., Bečková, I. and Farkaš, I. (2022) ‘Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks’, in arXiv. arXiv, pp. 1–10.
  281. Pommerening, F. et al. (2021) ‘Dantzig-Wolfe Decomposition for Cost Partitioning’, in ICAPS.
  282. Pratesi, F., Trasarti, R. and Giannotti, F. (2022) ‘Ethics in smart information systems’, in Ethical Evidence and Policymaking. Policy Press, pp. 162–184.
  283. Rajendran, P.T. et al. (2021) ‘Human-in-the-Loop Learning Methods Toward Safe DL-Based Autonomous Systems: A Review’, in Computer Safety, Reliability, and Security. SAFECOMP 2021 Workshops. Springer International Publishing, pp. 251–264.
  284. Rajendran, P.T. et al. (2022) ‘Human-in-the-loop Learning for Safe Exploration through Anomaly Prediction and Intervention’, in SafeAI@AAAI. CEUR-WS.org (CEUR Workshop Proceedings).
  285. Rakotoarison, H. et al. (2021) ‘Learning Meta-features for AutoML’, in International Conference on Learning Representations. Available at: https://openreview.net/forum?id=DTkEfj0Ygb8&referrer=%5Bthe%20profile%20of%20Herilalaina%20Rakotoarison%5D(%2Fprofile%3Fid%3D~Herilalaina_Rakotoarison1).
  286. Ramon Fraga Pereira, G.D.G., Frederico Messa, Andre Grahl Pereira (2022) ‘Iterative Depth-First Search for Fully Observable Non-Deterministic Planning’, in ICAPS.
  287. Reinboth, T. and Farkaš, I. (2022) ‘Ultimate Grounding of Abstract Concepts: A Graded Account’, in Journal of Cognition. Ubiquity Press, pp. 1–21.
  288. Resta, M., Monreale, A. and Bacciu, D. (2021) ‘Occlusion-Based Explanations in Deep Recurrent Models for Biomedical Signals’, Entropy, 23(8).
  289. Ribeiro, J.G. et al. (2021) ‘Helping People on the Fly: Ad Hoc Teamwork for Human-Robot Teams’, in G. Marreiros et al. (eds) Progress in Artificial Intelligence. Cham: Springer International Publishing, pp. 635–647.
  290. Risso, C.E. et al. (2021) ‘Exact Approach for Electric Vehicle Charging Infrastructure Location: A Real Case Study in Málaga, Spain’, in S. Nesmachnow and L. Hernández-Callejo (eds) Smart Cities – 4th Ibero-American Congress, ICSC-Cities 2021, Cancún,Mexico, November 29 – December 1, 2021, Revised Selected Papers. Springer (Communications in Computer and Information Science), pp. 42–57. Available at: https://doi.org/10.1007/978-3-030-96753-6_4.
  291. Rodrigo Gil-Merino, G.L., Enrique Alba, Jose Francisco Chicano, Zakaria Abdelmoiz Dahi (2021) ‘Quantum Computing: Present and Prospects’, in S.M. Enrique Alba Francisco Chicano, Gabriel Luque, Rodrigo Gil-Merino, Carlos Cotta, David Camacho, Manuel Ojeda-Aciego, Susana Montes, Alicia Troncoso, Jose Riquelme, Eva Onaindia, Maria Jose del Jesus, Jose Antonio Gamez, Alberto Bugarin, Mar Marcos, Agapito Ledezma, Juan Pedro Llerena, Javier Echanobe, Jamal Toutouh (ed.) 19th Conference of the Spanish Association for Artificial Intelligence (CAEPIA). CAEPIA, pp. 947–952.
  292. Rodriguez, I.D., Bonet, B., Sardiña, S., et al. (2021) ‘Flexible FOND Planning with Explicit Fairness Assumptions’, in ICAPS, pp. 290–298.
  293. Rodriguez, I.D., Bonet, B., Romero, J., et al. (2021) ‘Learning First-Order Representations for Planning from Black-Box States: New Results’, in KR.
  294. Rodriguez-Soto, M., López-Sánchez, M. and Rodríguez-Aguilar, J.A. (2021) ‘Multi-Objective Reinforcement Learning for Designing Ethical Environments’, in IJCAI. ijcai.org, pp. 545–551.
  295. Ronca, A. et al. (2022) ‘The Delay and Window Size Problems in Rule-Based Stream Reasoning’, Artificial Intelligence, p. 103668. Available at: https://doi.org/10.1016/j.artint.2022.103668.
  296. Ronca, A. and Giacomo, G.D. (2021a) ‘Efficient PAC Reinforcement Learning in Regular Decision Processes’, in IJCAI.
  297. Ronca, A. and Giacomo, G.D. (2021b) ‘Efficient PAC Reinforcement Learning in Regular Decision Processes’, in PRL Workshop.
  298. Ronca, A., Licks, G.P. and Giacomo, G.D. (2022) ‘Markov Abstractions for PAC Reinforcement Learning in Non-Markov Decision Processes’, in IJCAI. Available at: https://doi.org/10.24963/ijcai.2022/473.
  299. Sabbatini, F. and Calegari, R. (2022) ‘Symbolic Knowledge Extraction from Opaque Machine Learning Predictors: GridREx & PEDRO’, in G. Kern-Isberner, G. Lakemeyer, and T. Meyer (eds) 19th International Conference on Principles of Knowledge Representation and Reasoning (KR 2022). Haifa, Israel: IJCAI Organization, pp. 554–563. Available at: https://doi.org/10.24963/kr.2022/57.
  300. Sansone, E. (2022) ‘LSB: Local Self-Balancing MCMC in Discrete Spaces’, in International Conference on Machine Learning (ICML), pp. 19205–19220.
  301. Santos, L.R. de A. et al. (2021) ‘An LP-Based Approach for Goal Recognition as Planning’, in AAAI.
  302. Santos, P.M. et al. (2021) ‘Ad Hoc Teamwork in the Presence of Non-stationary Teammates’, in G. Marreiros et al. (eds) Progress in Artificial Intelligence. Cham: Springer International Publishing, pp. 648–660.
  303. Scavuzzo, L. et al. (2022) ‘Learning to Branch with Tree MDPs’, in Proceedings of the 36th International Conference on Neural Information Processing Systems (NeurIPS’22). New Orleans, LA, USA.
  304. Seccia, R. et al. (2022) ‘A Machine Learning Approach for 3D Load Feasibility Prediction’, in 32nd European Conference on Operational Research (EURO’22). Espoo, Finland.
  305. Segovia-Aguas, J. et al. (2022) ‘Scaling-up generalized planning as heuristic search with landmarks’, in Proceedings of the International Symposium on Combinatorial Search, pp. 171–179.
  306. Segovia-Aguas, J., Jiménez Celorrio, S. and Jonsson, A. (2022) ‘Computing Programs for Generalized Planning as Heuristic Search (Extended Abstract)’, in L.D. Raedt (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22. International Joint Conferences on Artificial Intelligence Organization, pp. 5334–5338. Available at: https://doi.org/10.24963/ijcai.2022/746.
  307. Segovia-Aguas, J., Jiménez, S. and Jonsson, A. (2021) ‘Generalized Planning as Heuristic Search’, in ICAPS, pp. 569–577.
  308. Seipp, J. (2021) ‘Online Saturated Cost Partitioning for Classical Planning’, in ICAPS.
  309. Seipp, J., Keller, T. and Helmert, M. (2021) ‘Saturated Post-hoc Optimization for Classical Planning’, in AAAI.
  310. Sempere, J.M. (2021) ‘On the Languages Accepted by Watson-Crick Finite Automata with Delays’, Mathematics, 9(8).
  311. Setzu, M. et al. (2021) ‘GLocalX – From Local to Global Explanations of Black Box AI Models’, Artif. Intell., 294, p. 103457. Available at: https://doi.org/10.1016/j.artint.2021.103457.
  312. Shamsabadi, A.S. et al. (2023) ‘Differentially private speaker anonymization’, Proceedings on Privacy Enhancing Technologies, 2023(1).
  313. Sievers, S., Gnad, D. and Torralba, Á. (2022) ‘Additive Pattern Databases for Decoupled Search’, in SOCS, pp. 180–189.
  314. Sievers, S. and Helmert, M. (2021) ‘Merge-and-Shrink: A Compositional Theory of Transformations of Factored Transition Systems’, JAIR, 71, pp. 781–883.
  315. Sievers, S. and Wehrle, M. (2021) ‘On Weak Stubborn Sets in Classical Planning’, in IJCAI, pp. 4167–4174.
  316. Simko, J., Racsko, P., et al. (2021) ‘A Study of Fake News Reading and Annotating in Social Media Context’, in New Review of Hypermedia and Multimedia. Taylor & Francis, pp. 97–127.
  317. Simko, J., Tomlein, M., et al. (2021) ‘Towards Continuous Automatic Audits of Social Media Adaptive Behavior and its Role in Misinformation Spreading’, in Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, pp. 411–414.
  318. Singh, A. et al. (2021) ‘Approximate Novelty Search’, in ICAPS, pp. 349–357.
  319. Skrlj, B. et al. (2022) ‘ReliefE: feature ranking in high-dimensional spaces via manifold embeddings’, Mach. Learn., 111(1), pp. 273–317. Available at: https://doi.org/10.1007/s10994-021-05998-5.
  320. Sokol, K. and Flach, P. (2021) ‘You Only Write Thrice: Creating Documents, Computational Notebooks and Presentations From a Single Source’, in Beyond static papers: Rethinking how we share scientific understanding in ML-ICLR 2021 workshop.
  321. Souza, M. de et al. (2021) ‘ACVIZ: A tool for the visual analysis of the configuration of algorithms with irace’, Operations Research Perspectives, 8, p. 100186. Available at: https://doi.org/10.1016/j.orp.2021.100186.
  322. Souza, M.D., Ritt, M. and López-Ibáñez, M. (2022) ‘Capping Methods for the Automatic Configuration of Optimization Algorithms’, Computers & Operations Research, 139, p. 105615. Available at: https://doi.org/10.1016/j.cor.2021.105615.
  323. Spallitta, G. et al. (2022) ‘SMT-based Weighted Model Integration with Structure Awareness’, in UAI.
  324. Speck, D. and Seipp, J. (2022) ‘New Refinement Strategies for Cartesian Abstractions’, in ICAPS.
  325. Srba, I. et al. (2022) ‘Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims’, in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, pp. 2949–2959.
  326. Ståhlberg, S., Bonet, B. and Geffner, H. (2022a) ‘Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits’, in ICAPS, pp. 629–637.
  327. Ståhlberg, S., Bonet, B. and Geffner, H. (2022b) ‘Learning Generalized Policies without Supervision Using GNNs’, in KR.
  328. Ståhlberg, S., Francès, G. and Seipp, J. (2021) ‘Learning Generalized Unsolvability Heuristics for Classical Planning’, in IJCAI, pp. 4175–4181.
  329. Steinmetz, M. et al. (2022) ‘Debugging a Policy: Automatic Action-Policy Testing in AI Planning’, in ICAPS, pp. 353–361.
  330. Straccia, U. and Casini, G. (2022) ‘A Minimal Deductive System for RDFS with Negative Statements’, in Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning, pp. 351–361. Available at: https://doi.org/10.24963/kr.2022/35.
  331. Suárez-Hernández, A., Andriella, A., et al. (2021) ‘Automatic learning of cognitive exercises for socially assistive robotics’, in 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN). IEEE, pp. 139–146.
  332. Suárez-Hernández, A., Segovia-Aguas, J., et al. (2021) ‘Online Action Recognition’, in AAAI.
  333. Sun, Z. et al. (2022) ‘Human Action Recognition From Various Data Modalities: A Review’, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–20. Available at: https://doi.org/10.1109/TPAMI.2022.3183112.
  334. Švec, P., Balogh, Š. and Homola, M. (2021) ‘Experimental Evaluation of Description Logic Concept Learning Algorithms for Static Malware Detection’, in Proceedings of the 7th International Conference on Information Systems Security and Privacy – Volume 1: ForSE. SciTePress, pp. 792–799.
  335. Szeląg, M. and Słowiński, R. (2022) ‘Dominance-based Rough Set Approach to Bank Customer Satisfaction Analysis’, in P. Jędrzejowicz et al. (eds) PP-RAI’2022, Proceedings of the 3rd Polish Conference on Artificial Intelligence, April 25-27, 2022, Gdynia, Poland. Gdynia, Poland: Publishing House of Gdynia Maritime University, pp. 147–150.
  336. Tamajka, M., Vesely, M. and Simko, M. (2022) ‘Optimizing Post-hoc Explainability Algorithm for Finding Faithful and Understandable Explanations for a Combination of Model, Task and Data’, in Proceedings of Workshop on explainable artificial intelligence XAI at IJCAI2022. International Joint Conferences on Artificial Intelligence Organization, pp. 103–110.
  337. Telang, P.R., Singh, M.P. and Yorke-Smith, N. (2021) ‘Maintenance of Social Commitments in Multiagent Systems’, in Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021. AAAI Press, pp. 11369–11377. Available at: https://ojs.aaai.org/index.php/AAAI/article/view/17355.
  338. Termos, A., Picascia, S. and Yorke-Smith, N. (2021a) ‘Agent-Based Simulation of West Asian Urban Dynamics: Impact of Refugees’, J. Artif. Soc. Soc. Simul., 24(1). Available at: https://doi.org/10.18564/jasss.4472.
  339. Termos, A., Picascia, S. and Yorke-Smith, N. (2021b) ‘Agent-Based Simulation of West Asian Urban Dynamics: Impact of Refugees’, J. Artif. Soc. Soc. Simul., 24(1). Available at: https://doi.org/10.18564/jasss.4472.
  340. Termos, A. and Yorke-Smith, N. (2022) ‘Urbanism and Geographic Crises: A Micro-Simulation Lens on Beirut’, Urban Planning, 7(1), pp. 87–100.
  341. Teso, S. et al. (2022) ‘Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens’, CoRR, abs/2205.10157. Available at: https://doi.org/10.48550/arXiv.2205.10157.
  342. Teso, S. and Vergari, A. (2022) ‘Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs’, The AAAI-22 Workshop on Interactive Machine Learning [Preprint].
  343. Tianjiao Li, J., Qiuhong Ke, Hossein Rahmani, Rui En Ho, Henghui Ding (2021) ‘Else-Net: Elastic Semantic Network for Continual Action Recognition from Skeleton Data’, in International Conference on Computer Vision.
  344. Tianjiao Li, J.L., Lin Geng Foo, Qiuhong Ke, Hossein Rahmani, Anran Wang, Jinghua Wang (2022) ‘Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition’, in European Conference on Computer Vision.
  345. Tiger, M. et al. (2021) ‘Enhancing Lattice-Based Motion Planning With Introspective Learning and Reasoning’, IEEE Robotics and Automation Letters, 6(3), pp. 4385–4392.
  346. Tomlein, M. et al. (2021) ‘An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes’, in Proceedings of the 15th ACM Conference on Recommender Systems. Association for Computing Machinery, pp. 1–11.
  347. Tomlein, M. et al. (2022) ‘Black-box Audit of YouTube’s Video Recommendation: Investigation of Misinformation Filter Bubble Dynamics (Extended Abstract)’, in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22. International Joint Conferences on Artificial Intelligence Organization, pp. 5349–5353.
  348. Torralba, Á., Seipp, J. and Sievers, S. (2021) ‘Automatic Instance Generation for Classical Planning’, in ICAPS.
  349. Toutouh, J. and Alba, E. (2022) ‘A Low Cost IoT Cyber-Physical System for Vehicle and Pedestrian Tracking in a Smart Campus’, Sensors, 22(17). Available at: https://doi.org/10.3390/s22176585.
  350. Toutouh, J., Lebrusán, I. and Cintrano, C. (2021) ‘Using Open Data to Analyze Public Bus Service from an Age Perspective: Melilla Case’, in S. Nesmachnow and L. Hernández-Callejo (eds) Smart Cities – 4th Ibero-American Congress, ICSC-Cities 2021, Cancún, Mexico, November 29 – December 1, 2021, Revised Selected Papers. Springer (Communications in Computer and Information Science), pp. 223–239. Available at: https://doi.org/10.1007/978-3-030-96753-6_16.
  351. Tschammer, J. von, Mattmüller, R. and Speck, D. (2022) ‘Loopless Top-k Planning’, in ICAPS.
  352. Uhliarik, I. (2022) ‘Enhancing and Evaluating the Product Fuzzy DPLL Solver’, SN Comput. Sci., 3(5), p. 388. Available at: https://doi.org/10.1007/s42979-022-01192-z.
  353. Ullah, I. et al. (2022) ‘Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification’, in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks. (NIPS’22).
  354. Umili, E. et al. (2021) ‘Learning a Symbolic Planning Domain through the Interaction with Continuous Environments’, in PRL Workshop.
  355. V Policastro, I.D.F., D. Righelli, A. Carissimo, L. Cutillo (2021) ‘ROBustness In Network (robin): an R Package for Comparison and Validation of Communities’, The R Journal, 13(1), pp. 292–309.
  356. Valenti, A., Berti, S. and Bacciu, D. (2021) ‘Calliope – A Polyphonic Music Transformer’, CoRR, abs/2107.05546. Available at: https://arxiv.org/abs/2107.05546.
  357. Ventola, F., Dhami, D.S. and Kersting, K. (2021) ‘Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits’, in Inductive Logic Programming – 30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings, pp. 251–265.
  358. Vergari, A. et al. (2021) ‘A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-theoretic Queries’, in NeurIPS. Curran Associates.
  359. Vidal, G. (2022) ‘Explanations as Programs in Probabilistic Logic Programming’, in FLOPS. Springer (Lecture Notes in Computer Science), pp. 205–223.
  360. Viehmann, T., Hofmann, T. and Lakemeyer, G. (2021) ‘Transforming Robotic Plans with Timed Automata to Solve Temporal Platform Constraints’, in IJCAI. ijcai.org, pp. 2083–2089.
  361. Vital, F. et al. (2022) ‘Perceive, Represent, Generate: Translating Multimodal Information to Robotic Motion Trajectories’, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
  362. Wiegel, E. and Yorke-Smith, N. (2022) ‘No Hope for First-Time Buyers? Towards Agent-Based Market Analysis of Urban Housing Balance’, in Working Notes of AAMAS’22 Workshop on Agent Based Modelling of Urban Systems. Auckland, New Zealand (virtual).
  363. Wietrzykowski, J. and Skrzypczy\’nski, P. (2021) ‘On the descriptive power of LiDAR intensity images for segment-based loop closing in 3-D SLAM’, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
  364. Winters, T. et al. (2022) ‘DeepStochLog: Neural Stochastic Logic Programming’, in AAAI. AAAI Press, pp. 10090–10100.
  365. Xiao, S. et al. (2021) ‘On-the-fly Synthesis for LTL over Finite Traces’, in AAAI.
  366. Yan, S. et al. (2021) ‘NAS-Bench-x11 and the Power of Learning Curves’, in Advances in Neural Information Processing Systems.
  367. Yang, W.-C., Raskin, J.-F. and De Raedt, L. (2021) ‘Lifted Model Checking for Relational MDPs’, Machine Learning [Preprint].
  368. Yilmaz, K. and Yorke-Smith, N. (2021) ‘A Study of Learning Search Approximation in Mixed Integer Branch and Bound: Node Selection in SCIP’, AI, 2(2), pp. 150–178.
  369. Yunsheng Pang, J.L., Qiuhong Ke, Hossein Rahmani, James Bailey (2022) ‘IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition’, in European Conference on Computer Vision.
  370. Zecevic, M. et al. (2021) ‘Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models’, in Advances in Neural Information Processing Systems.
  371. Zervakis, G. et al. (2021) ‘On Refining BERT Contextualized Embeddings using Semantic Lexicons’, in ECML/PKDD2021 Workshop on Combination of Symbolic and Sub-symbolic Methods and their Applications. Available at: https://hal.archives-ouvertes.fr/hal-03318571.
  372. Zervakis, G. et al. (2022) ‘An analogy based approach for solving target sense verification’, in 6th International Conference on Natural Language Processing and Information Retrieval.
  373. Zhu, S. et al. (2021) ‘On the Power of Automata Minimization in Temporal Synthesis’, in GandALF. (EPTCS), pp. 117–134.
  374. Zhu, S. and Giacomo, G.D. (2022a) ‘Act for Your Duties but Maintain Your Rights’, in KR.
  375. Zhu, S. and Giacomo, G.D. (2022b) ‘Synthesis of Maximally Permissive Strategies for LTLf Specifications’, in IJCAI. ijcai.org, pp. 2783–2789.
  376. Zuzana Hlávková, P.K., Martin Homola and Pukancová, J. (2022) ‘An API for DL Abduction Solvers (Extended Abstract)’, in The Third Workshop on Explainable Logic-Based Knowledge Representation (XLoKR 2022).

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