Scientific Publications

Conference papers/workshops

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  TAILOR Scientists at ECAI 2023 in Krakow11 September 2023
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  TAILOR Scientists at IJCAI 2023 in Macao30 May 2023
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  TAILOR scientists at AAMAS and CVPR27 March 2023
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  Accepted papers by TAILOR scientists to AAAI 202323 December 2022
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  TAILOR scientists at NeurIPS 202217 October 2022
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  Accepted papers by TAILOR scientists to IJCAI-ECAI 202230 June 2022
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  A TAILOR paper selected for oral presentation at CVPR 202220 May 2022
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  AAAI 2022: accepted papers from the TAILOR Network21 January 2022

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).
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