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Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Perspectives Workshop 22282)

Authors James P. Delgrande, Birte Glimm, Thomas Meyer, Miroslaw Truszczynski, Frank Wolter

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Author Details

James P. Delgrande
  • Simon Fraser University, CA
Birte Glimm
  • Ulm University, DE
Thomas Meyer
  • University of Cape Town, ZA
Miroslaw Truszczynski
  • University of Kentucky, US
Frank Wolter
  • University of Liverpool, GB

Acknowledgements

We thank the Dagstuhl staff for their outstanding administrative and operational support in the organisation and running of the workshop. We thank the participants of the workshop, without whose involvement this manifesto, of course, would not be possible. We also thank those individuals who helped us with parts of this manifesto: Vaishak Belle (Reasoning under Uncertainty), Diego Calvanese (Information Systems), Jens Claßen (Reasoning about Action), Giuseppe de Giacomo (Reasoning about Action), Wolfgang Dvořák (Argumentation), Anthony Hunter (Argumentation), Gerhard Lakemeyer (KR and Robotics), Marco Montali (Information Systems), Ana Ozaki (KR and ML), Francesco Ricca (ASP), Steven Schokaert (KR and ML), Francesca Toni (Argumentation), Johannes Wallner (Argumentation), Stefan Woltran (Argumentation).

Cite AsGet BibTex

James P. Delgrande, Birte Glimm, Thomas Meyer, Miroslaw Truszczynski, and Frank Wolter. Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Perspectives Workshop 22282). In Dagstuhl Manifestos, Volume 10, Issue 1, pp. 1-61, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/DagMan.10.1.1

Abstract

Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022,sser a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade.

Subject Classification

ACM Subject Classification
  • Theory of computation → Semantics and reasoning
  • Theory of computation → Logic
  • Theory of computation → Complexity theory and logic
  • Information systems → Information integration
  • Computing methodologies → Artificial intelligence
  • Computing methodologies → Knowledge representation and reasoning
Keywords
  • Knowledge representation and reasoning
  • Applications of logics
  • Declarative representations
  • Formal logic

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References

  1. M. Abadi and J.Y. Halpern. Decidability and expressiveness for first-order logics of probability. Information and Computation, 112(1):1-36, 1994. URL: https://doi.org/10.1006/inco.1994.1049.
  2. Ralph Abboud, İsmail İlkan Ceylan, and Thomas Lukasiewicz. Learning to reason: Leveraging neural networks for approximate DNF counting. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, pages 3097-3104. AAAI Press, 2020. URL: https://ojs.aaai.org/index.php/AAAI/article/view/5705, URL: https://doi.org/10.1609/AAAI.V34I04.5705.
  3. Dirk Abels, Julian Jordi, Max Ostrowski, Torsten Schaub, Ambra Toletti, and Philipp Wanko. Train scheduling with hybrid answer set programming. Theory and Practice of Logic Programming, 21(3):317-347, 2021. URL: https://doi.org/10.1017/S1471068420000046.
  4. Mirza Mohtashim Alam, Md. Rashad Al Hasan Rony, Mojtaba Nayyeri, Karishma Mohiuddin, M. S. T. Mahfuja Akter, Sahar Vahdati, and Jens Lehmann. Language model guided knowledge graph embeddings. IEEE Access, 10:76008-76020, 2022. URL: https://doi.org/10.1109/ACCESS.2022.3191666.
  5. Jesse Alama, Tom Heskes, Daniel Kühlwein, Evgeni Tsivtsivadze, and Josef Urban. Premise selection for mathematics by corpus analysis and kernel methods. Journal of Automated Reasoning, 52(2):191-213, 2014. URL: https://doi.org/10.1007/s10817-013-9286-5.
  6. Carlos E Alchourrón, Peter Gärdenfors, and David Makinson. On the Logic of Theory Change: Partial Meet Contraction and Revision Functions. The Journal of Symbolic Logic, 50(2):510-530, 1985. URL: https://doi.org/10.2307/2274239.
  7. Mario Alviano, Wolfgang Faber, and Martin Gebser. Aggregate semantics for propositional answer set programs. CoRR, abs/2109.08662, 2021. https://arxiv.org/abs/2109.08662, URL: https://doi.org/10.48550/arXiv.2109.08662.
  8. Medina Andresel, Julien Corman, Magdalena Ortiz, Juan L. Reutter, Ognjen Savkovic, and Mantas Simkus. Stable model semantics for recursive SHACL. In WWW '20: The Web Conference 2020, pages 1570-1580, 2020. URL: https://doi.org/10.1145/3366423.3380229.
  9. Renzo Angles, Marcelo Arenas, Pablo Barceló, Aidan Hogan, Juan L. Reutter, and Domagoj Vrgoc. Foundations of modern query languages for graph databases. ACM Computing Surveys, 50(5):68:1-68:40, 2017. URL: https://doi.org/10.1145/3104031.
  10. Charles Antaki and Ivan Leudar. Explaining in conversation: Towards an argument model. European Journal of Social Psychology, 22(2):181-194, 1992. Google Scholar
  11. Marcelo Arenas, Pablo Barceló, Leonid Libkin, and Filip Murlak. Foundations of Data Exchange. Cambridge University Press, 2014. URL: http://www.cambridge.org/9781107016163.
  12. Marcelo Arenas, Gonzalo I. Diaz, and Egor V. Kostylev. Reverse engineering SPARQL queries. In Jacqueline Bourdeau, Jim Hendler, Roger Nkambou, Ian Horrocks, and Ben Y. Zhao, editors, Proceedings of the 25th International Conference on World Wide Web, WWW 2016, pages 239-249. ACM, 2016. URL: https://doi.org/10.1145/2872427.2882989.
  13. Alessandro Artale and Enrico Franconi. Foundations of temporal conceptual data models. In Alexander Borgida, Vinay K. Chaudhri, Paolo Giorgini, and Eric S. K. Yu, editors, Conceptual Modeling: Foundations and Applications - Essays in Honor of John Mylopoulos, volume 5600 of Lecture Notes in Computer Science, pages 10-35. Springer, 2009. URL: https://doi.org/10.1007/978-3-642-02463-4_2.
  14. Alessandro Artale, Roman Kontchakov, Alisa Kovtunova, Vladislav Ryzhikov, Frank Wolter, and Michael Zakharyaschev. First-order rewritability and complexity of two-dimensional temporal ontology-mediated queries. Journal of Artificial Intelligence Research, 75:1223-1291, 2022. URL: https://doi.org/10.1613/jair.1.13511.
  15. Masataro Asai and Christian Muise. Learning neural-symbolic descriptive planning models via cube-space priors: The voyage home (to STRIPS). In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pages 2676-2682. ijcai.org, 2020. URL: https://doi.org/10.24963/ijcai.2020/371.
  16. Katie Atkinson and Trevor J M Bench-Capon. Argumentation schemes in AI and law. Argument and Computation, 12(3):417-434, 2021. URL: https://doi.org/10.3233/AAC-200543.
  17. Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, and Sunita Sarawagi. Learning from rules generalizing labeled exemplars. In 8th International Conference on Learning Representations, ICLR 2020. OpenReview.net, 2020. URL: https://openreview.net/forum?id=SkeuexBtDr.
  18. F. Baader, D. Calvanese, D. McGuinness, D. Nardi, and P Patel-Schneider, editors. The Description Logic Handbook. Cambridge University Press, Cambridge, second edition, 2007. Google Scholar
  19. Franz Baader, Sebastian Brandt, and Carsten Lutz. Pushing the EL envelope. In Leslie Pack Kaelbling and Alessandro Saffiotti, editors, IJCAI-05, Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, UK, July 30 - August 5, 2005, pages 364-369. Professional Book Center, 2005. URL: http://ijcai.org/Proceedings/05/Papers/0372.pdf.
  20. Franz Baader, Diego Calvanese, Deborah L. McGuinness, Daniele Nardi, and Peter F. Patel-Schneider, editors. The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, 2003. Google Scholar
  21. Franz Baader, Silvio Ghilardi, and Carsten Lutz. LTL over description logic axioms. ACM Transactions on Computational Logic, 13(3):21:1-21:32, 2012. URL: https://doi.org/10.1145/2287718.2287721.
  22. Franz Baader, Ian Horrocks, Carsten Lutz, and Ulrike Sattler. An Introduction to Description Logic. Cambridge University Press, 2017. URL: http://www.cambridge.org/de/academic/subjects/computer-science/knowledge-management-databases-and-data-mining/introduction-description-logic?format=PB#17zVGeWD2TZUeu6s.97.
  23. Franz Baader, Ian Horrocks, and Ulrike Sattler. Description logics. In Frank van Harmelen, Vladimir Lifschitz, and Bruce W. Porter, editors, Handbook of Knowledge Representation, volume 3 of Foundations of Artificial Intelligence, pages 135-179. Elsevier, 2008. URL: https://doi.org/10.1016/S1574-6526(07)03003-9.
  24. Franz Baader, Francesco Kriegel, Adrian Nuradiansyah, and Rafael Peñaloza. Making repairs in description logics more gentle. In Michael Thielscher, Francesca Toni, and Frank Wolter, editors, Principles of Knowledge Representation and Reasoning: Proceedings of the Sixteenth International Conference, KR 2018, pages 319-328. AAAI Press, 2018. URL: https://aaai.org/ocs/index.php/KR/KR18/paper/view/18056.
  25. Samy Badreddine, Artur d'Avila Garcez, Luciano Serafini, and Michael Spranger. Logic tensor networks. Artificial Intelligence, 303:103649, 2022. URL: https://doi.org/10.1016/j.artint.2021.103649.
  26. Ivana Balazevic, Carl Allen, and Timothy M. Hospedales. Tucker: Tensor factorization for knowledge graph completion. In Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan, editors, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, pages 5184-5193. Association for Computational Linguistics, 2019. URL: https://doi.org/10.18653/v1/D19-1522.
  27. Mutsunori Banbara, Katsumi Inoue, Benjamin Kaufmann, Tenda Okimoto, Torsten Schaub, Takehide Soh, Naoyuki Tamura, and Philipp Wanko. teaspoon: solving the curriculum-based course timetabling problems with answer set programming. Annals of Operations Research, 275(1):3-37, 2019. URL: https://doi.org/10.1007/s10479-018-2757-7.
  28. Bita Banihashemi, Giuseppe De Giacomo, and Yves Lespérance. Abstraction of agents executing online and their abilities in the situation calculus. In Jérôme Lang, editor, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, pages 1699-1706. ijcai.org, 2018. URL: https://doi.org/10.24963/ijcai.2018/235.
  29. Chitta Baral, Thomas Bolander, Hans van Ditmarsch, and Sheila McIlrath. Epistemic Planning (Dagstuhl Seminar 17231). Dagstuhl Reports, 7(6):1-47, 2017. URL: https://doi.org/10.4230/DagRep.7.6.1.
  30. Pietro Baroni, Dov Gabbay, Massimiliano Giacomin, and Leendert van der Torre, editors. Handbook of Formal Argumentation. College Publications, 2018. Google Scholar
  31. Pietro Baroni and Massimiliano Giacomin. On principle-based evaluation of extension-based argumentation semantics. Artificial Intelligence, 171(10):675-700, 2007. Argumentation in Artificial Intelligence. URL: https://doi.org/10.1016/j.artint.2007.04.004.
  32. V. Belle and G. Lakemeyer. Reasoning about probabilities in unbounded first-order dynamical domains. In IJCAI, 2017. URL: https://doi.org/10.24963/ijcai.2017/115.
  33. Vaishak Belle. Logic meets probability: Towards explainable AI systems for uncertain worlds. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, pages 5116-5120, 2017. URL: https://doi.org/10.24963/ijcai.2017/733.
  34. Vaishak Belle. Logic meets learning: From aristotle to neural networks. In Pascal Hitzler and Md Kamruzzaman Sarker, editors, Neuro-Symbolic Artificial Intelligence - The State of the Art, Frontiers in Artificial Intelligence and Applications, pages 78-102. IOS Press, Netherlands, jan 2022. URL: https://doi.org/10.3233/FAIA210350.
  35. Vaishak Belle, Thomas Bolander, Andreas Herzig, and Bernhard Nebel. Epistemic planning: Perspectives on the special issue. Artificial Intelligence, page 103842, 2022. URL: https://doi.org/10.1016/j.artint.2022.103842.
  36. Vaishak Belle and Brendan Juba. Implicitly learning to reason in first-order logic. Advances in Neural Information Processing Systems, 32, 2019. URL: https://proceedings.neurips.cc/paper/2019/hash/09fb05dd477d4ae6479985ca56c5a12d-Abstract.html.
  37. Luigi Bellomarini, Emanuel Sallinger, and Georg Gottlob. The vadalog system: Datalog-based reasoning for knowledge graphs. Proc. VLDB Endow., 11(9):975-987, 2018. URL: https://doi.org/10.14778/3213880.3213888.
  38. Michael Benedikt, Kristian Kersting, Phokion G. Kolaitis, and Daniel Neider. Logic and Learning (Dagstuhl Seminar 19361). Dagstuhl Reports, 9(9):1-22, 2020. URL: https://doi.org/10.4230/DagRep.9.9.1.
  39. Philippe Besnard and Anthony Hunter. Elements of argumentation, volume 47. MIT press Cambridge, 2008. URL: http://mitpress.mit.edu/books/elements-argumentation.
  40. Philippe Besnard and Anthony Hunter. A review of argumentation based on deductive arguments. In Pietro Baroni, Dov Gabbay, Massimilino Giacomin, and Leendert Van der Torre, editors, Handbook of Formal Argumentation, pages 437-484. College Publications, 2018. Google Scholar
  41. N. Bidoit and C. Froidevaux. Minimalism subsumes default logic and circumscription. In Proceedings of IEEE Symposium on Logic in Computer Science, LICS-87, pages 89-97. IEEE Press, 1987. Google Scholar
  42. N. Bidoit and C. Froidevaux. Negation by default and unstratifiable logic programs. Theoretical Computer Science, 78(1, (Part B)):85-112, 1991. URL: https://doi.org/10.1016/0304-3975(51)90004-7.
  43. Meghyn Bienvenu. A short survey on inconsistency handling in ontology-mediated query answering. Künstliche Intelligenz, 34(4):443-451, 2020. URL: https://doi.org/10.1007/s13218-020-00680-9.
  44. Meghyn Bienvenu and Magdalena Ortiz. Ontology-mediated query answering with data-tractable description logics. In Wolfgang Faber and Adrian Paschke, editors, Reasoning Web. Web Logic Rules - 11th International Summer School, volume 9203 of Lecture Notes in Computer Science, pages 218-307. Springer, 2015. URL: https://doi.org/10.1007/978-3-319-21768-0_9.
  45. Meghyn Bienvenu, Balder ten Cate, Carsten Lutz, and Frank Wolter. Ontology-Based Data Access: A Study through Disjunctive Datalog, CSP, and MMSNP. ACM Transactions on Database Systems, 39(4):33:1-33:44, 2014. URL: https://doi.org/10.1145/2661643.
  46. D. Bobrow, editor. Special Issue on Nonmonotonic Reasoning, volume 13(1-2). Elsevier, 1980. Google Scholar
  47. Thomas Bolander, Lasse Dissing, and Nicolai Herrmann. Del-based epistemic planning for human-robot collaboration: Theory and implementation. In Meghyn Bienvenu, Gerhard Lakemeyer, and Esra Erdem, editors, Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning, KR 2021, pages 120-129, 2021. URL: https://doi.org/10.24963/kr.2021/12.
  48. Mathilde Boltenhagen, Thomas Chatain, and Josep Carmona. Optimized SAT encoding of conformance checking artefacts. Computing, 103(1):29-50, 2021. URL: https://doi.org/10.1007/s00607-020-00831-8.
  49. Glauber De Bona, John Grant, Anthony Hunter, and Sébastien Konieczny. Classifying inconsistency measures using graphs. J. Artif. Intell. Res., 66:937-987, 2019. URL: https://doi.org/10.1613/JAIR.1.11852.
  50. Piero A. Bonatti. Rational closure for all description logics. Artificial Intelligence, 274:197-223, 2019. URL: https://doi.org/10.1016/j.artint.2019.04.001.
  51. Piero Andrea Bonatti, Iliana Mineva Petrova, and Luigi Sauro. Defeasible reasoning in description logics: An overview on DLN. In Giuseppe Cota, Marilena Daquino, and Gian Luca Pozzato, editors, Applications and Practices in Ontology Design, Extraction, and Reasoning, volume 49 of Studies on the Semantic Web, pages 178-193. IOS Press, 2020. URL: https://doi.org/10.3233/SSW200043.
  52. Andrei Bondarenko, Phan Minh Dung, Robert A Kowalski, and Francesca Toni. An abstract, argumentation-theoretic approach to default reasoning. Artificial intelligence, 93(1-2):63-101, 1997. URL: https://doi.org/10.1016/S0004-3702(97)00015-5.
  53. Richard Booth and Thomas Andreas Meyer. Admissible and restrained revision. Journal of Artificial Intelligence Research, 26:127-151, 2006. URL: https://doi.org/10.1613/jair.1874.
  54. Richard Booth, Thomas Andreas Meyer, Ivan José Varzinczak, and Renata Wassermann. On the link between partial meet, kernel, and infra contraction and its application to horn logic. Journal of Artificial Intelligence Research, 42:31-53, 2011. URL: https://doi.org/10.1613/jair.3364.
  55. Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. In Christopher J. C. Burges, Léon Bottou, Zoubin Ghahramani, and Kilian Q. Weinberger, editors, Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems, pages 2787-2795, 2013. URL: https://proceedings.neurips.cc/paper/2013/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html.
  56. Stefan Borgwardt, Jörg Hoffmann, Alisa Kovtunova, Markus Krötzsch, Bernhard Nebel, and Marcel Steinmetz. Expressivity of planning with horn description logic ontologies. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, pages 5503-5511. AAAI Press, 2022. URL: https://doi.org/10.1609/AAAI.V36I5.20489.
  57. Elena Botoeva, Boris Konev, Carsten Lutz, Vladislav Ryzhikov, Frank Wolter, and Michael Zakharyaschev. Inseparability and conservative extensions of description logic ontologies: A survey. In Jeff Z. Pan, Diego Calvanese, Thomas Eiter, Ian Horrocks, Michael Kifer, Fangzhen Lin, and Yuting Zhao, editors, Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering - 12th International Summer School 2016, volume 9885 of Lecture Notes in Computer Science, pages 27-89. Springer, 2016. URL: https://doi.org/10.1007/978-3-319-49493-7_2.
  58. R.J. Brachman and H.J. Levesque. Expressiveness and tractability in knowledge representation and reasoning. Computational Intelligence, 3(2):78-93, 1987. URL: https://doi.org/10.1111/j.1467-8640.1987.tb00176.x.
  59. R.J. Brachman and H.J. Levesque. Knowledge Representation and Reasoning. Morgan-Kaufmann, 2004. Google Scholar
  60. R.J. Brachman and H.J. Levesque. Machines Like Us: Toward AI with Common Sense. The MIT Press, Cambridge, MA, 2022. Google Scholar
  61. R.J. Brachman and B.C. Smith. Special issue on knowledge representation. SIGART Newsletter, 70, 1980. URL: https://doi.org/10.1145/1056751.1056752.
  62. Ronald J. Brachman and James G. Schmolze. An Overview of the KL-ONE Knowledge Representation System. Cognitive Science, 9:171-216, 1985. URL: https://doi.org/10.1207/s15516709cog0902_1.
  63. Michael Brenner and Bernhard Nebel. Continual planning and acting in dynamic multiagent environments. Auton. Agents Multi Agent Syst., 19(3):297-331, 2009. URL: https://doi.org/10.1007/s10458-009-9081-1.
  64. G. Brewka, I. Niemelä, and M. Truszczynski. Nonmonotonic reasoning. In F. van Harmelen, V. Lifschitz, and B. Porter, editors, Handbook of Knowledge Representation, pages 239-284. Elsevier Science, San Diego, USA, 2008. URL: https://doi.org/10.1016/S1574-6526(07)03006-4.
  65. Gerhard Brewka, Thomas Eiter, and Mirosław Truszczyński. Answer Set Programming at a Glance. Communications of the ACM, 54(12):92-103, 2011. URL: https://doi.org/10.1145/2043174.2043195.
  66. Gerhard Brewka, Thomas Eiter, and Miroslaw Truszczynski. Answer set programming: An introduction to the special issue. AI Mag., 37(3):5-6, 2016. URL: https://doi.org/10.1609/aimag.v37i3.2669.
  67. Katarina Britz, Giovanni Casini, Thomas Meyer, Kody Moodley, Uli Sattler, and Ivan Varzinczak. Principles of KLM-style defeasible description logics. ACM Transactions on Computational Logic, 22(1), 2020. URL: https://doi.org/10.1145/3420258.
  68. Guy Van den Broeck, Ingo Thon, Martijn van Otterlo, and Luc De Raedt. Dtproblog: A decision-theoretic probabilistic prolog. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI'10, pages 1217-1222. AAAI Press, 2010. URL: https://doi.org/10.1609/aaai.v24i1.7755.
  69. Bruce Buchanan and Edward A. Feigenbaum. Dendral and Meta-dendral: Their Applications Dimension. In Readings in artificial intelligence, pages 313-322. Elsevier, 1981. Google Scholar
  70. Bruce Buchanan and Edward Shortliffe. Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison Wesley, 1984. Google Scholar
  71. Bruce Buchanan, Georgia Sutherland, and Edward A.Feigenbaum. Heuristic DENDRAL: A Program for Generating Explanatory Hypotheses. Organic Chemistry, 1969. Google Scholar
  72. Wolfram Burgard, Armin B. Cremers, Dieter Fox, Dirk Hähnel, Gerhard Lakemeyer, Dirk Schulz, Walter Steiner, and Sebastian Thrun. Experiences with an interactive museum tour-guide robot. Artificial Intelligence, 114(1-2):3-55, 1999. URL: https://doi.org/10.1016/S0004-3702(99)00070-3.
  73. Paula-Andra Busoniu, Johannes Oetsch, Jörg Pührer, Peter Skocovsky, and Hans Tompits. Sealion: An eclipse-based IDE for answer-set programming with advanced debugging support. Theory and Practice of Logic Programming, 13(4-5):657-673, 2013. URL: https://doi.org/10.1017/S1471068413000410.
  74. Pedro Cabalar, Martín Diéguez, Torsten Schaub, and Anna Schuhmann. Metric temporal answer set programming over timed traces. In Georg Gottlob, Daniela Inclezan, and Marco Maratea, editors, Logic Programming and Nonmonotonic Reasoning - 16th International Conference, LPNMR 2022, volume 13416 of Lecture Notes in Computer Science, pages 117-130. Springer, 2022. URL: https://doi.org/10.1007/978-3-031-15707-3_10.
  75. Andrea Calì, Georg Gottlob, and Andreas Pieris. Towards more expressive ontology languages: The query answering problem. Artificial Intelligence, 193:87-128, 2012. URL: https://doi.org/10.1016/j.artint.2012.08.002.
  76. Francesco Calimeri, Wolfgang Faber, Martin Gebser, Giovambattista Ianni, Roland Kaminski, Thomas Krennwallner, Nicola Leone, Marco Maratea, Francesco Ricca, and Torsten Schaub. ASP-Core-2 input language format. Theory and Practice of Logic Programming, 20(2):294-309, 2020. URL: https://doi.org/10.1017/S1471068419000450.
  77. Francesco Calimeri, Davide Fuscà, Simona Perri, and Jessica Zangari. I-DLV: The new intelligent grounder of DLV. In Giovanni Adorni, Stefano Cagnoni, Marco Gori, and Marco Maratea, editors, AI*IA 2016: XVth International Conference of the Italian Association for Artificial Intelligence, volume 10037 of Lecture Notes in Computer Science, pages 192-207. Springer, 2016. URL: https://doi.org/10.1007/978-3-319-49130-1_15.
  78. Diego Calvanese, Giuseppe De Giacomo, Domenico Lembo, Maurizio Lenzerini, and Riccardo Rosati. Tractable reasoning and efficient query answering in description logics: The DL-Lite family. Journal of Automated Reasoning, 39(3):385-429, 2007. URL: https://doi.org/10.1007/s10817-007-9078-x.
  79. Diego Calvanese, Giuseppe De Giacomo, Domenico Lembo, Maurizio Lenzerini, and Riccardo Rosati. Data complexity of query answering in description logics. Artificial Intelligence, 195:335-360, 2013. URL: https://doi.org/10.1016/j.artint.2012.10.003.
  80. Diego Calvanese, Giuseppe De Giacomo, and Marco Montali. Foundations of data-aware process analysis: a database theory perspective. In Richard Hull and Wenfei Fan, editors, Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2013, pages 1-12. ACM, 2013. URL: https://doi.org/10.1145/2463664.2467796.
  81. Diego Calvanese, Giuseppe De Giacomo, Marco Montali, and Fabio Patrizi. First-order μ-calculus over generic transition systems and applications to the situation calculus. Information and Computation, 259(3):328-347, 2018. URL: https://doi.org/10.1016/j.ic.2017.08.007.
  82. Oana-Maria Camburu, Brendan Shillingford, Pasquale Minervini, Thomas Lukasiewicz, and Phil Blunsom. Make up your mind! adversarial generation of inconsistent natural language explanations. In Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault, editors, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, pages 4157-4165. Association for Computational Linguistics, 2020. URL: https://doi.org/10.18653/v1/2020.acl-main.382.
  83. Angelo Cangelosi and Minoru Asada. Cognitive Robotics. The MIT Press, may 2022. URL: https://doi.org/10.7551/mitpress/13780.001.0001.
  84. Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R. Hruschka Jr., and Tom M. Mitchell. Toward an architecture for never-ending language learning. In Maria Fox and David Poole, editors, Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010. AAAI Press, 2010. URL: https://doi.org/10.1609/AAAI.V24I1.7519.
  85. David Carral, Irina Dragoste, Larry González, Ceriel J. H. Jacobs, Markus Krötzsch, and Jacopo Urbani. Vlog: A rule engine for knowledge graphs. In Chiara Ghidini, Olaf Hartig, Maria Maleshkova, Vojtech Svátek, Isabel F. Cruz, Aidan Hogan, Jie Song, Maxime Lefrançois, and Fabien Gandon, editors, The Semantic Web - ISWC 2019 - 18th International Semantic Web Conference, volume 11779 of Lecture Notes in Computer Science, pages 19-35. Springer, 2019. URL: https://doi.org/10.1007/978-3-030-30796-7_2.
  86. Michael Cashmore, Maria Fox, Derek Long, Daniele Magazzeni, Bram Ridder, Arnau Carrera, Narcís Palomeras, Natàlia Hurtós, and Marc Carreras. Rosplan: Planning in the robot operating system. In Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling, ICAPS 2015, pages 333-341, 2015. URL: https://doi.org/10.1609/icaps.v25i1.13699.
  87. Giovanni Casini, Thomas Meyer, and Ivan Varzinczak. Rational defeasible belief change. In Diego Calvanese, Esra Erdem, and Michael Thielscher, editors, Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning, KR 2020, pages 213-222, 2020. URL: https://doi.org/10.24963/kr.2020/22.
  88. Federico Cerutti, Sarah A Gaggl, Matthias Thimm, and Johannes Wallner. Foundations of implementations for formal argumentation. In Pietro Baroni, Dov Gabbay, Massimilino Giacomin, and Leendert Van der Torre, editors, Handbook of Formal Argumentation, pages 688-767. College Publications, 2018. Google Scholar
  89. Günther Charwat, Wolfgang Dvořák, Sarah A Gaggl, Johannes P Wallner, and Stefan Woltran. Methods for solving reasoning problems in abstract argumentation-a survey. Artificial intelligence, 220:28-63, 2015. URL: https://doi.org/10.1016/j.artint.2014.11.008.
  90. Vinay Chaudhri, Chaitanya Baru, Naren Chittar, Xin Dong, Michael Genesereth, James Hendler, Aditya Kalyanpur, Douglas Lenat, Juan Sequeda, Denny Vrandečić, and Kuansan Wang. Knowledge graphs: Introduction, history and, perspectives. AI Magazine, 43(1):17-29, 2022. URL: https://doi.org/10.1609/aimag.v43i1.19119.
  91. Jiaoyan Chen, Yuan He, Ernesto Jiménez-Ruiz, Hang Dong, and Ian Horrocks. Contextual semantic embeddings for ontology subsumption prediction. CoRR, abs/2202.09791, 2022. URL: https://doi.org/10.48550/arXiv.2202.09791.
  92. Juan Chen, Anthony G. Cohn, Dayou Liu, Sheng-sheng Wang, Jihong Ouyang, and Qiangyuan Yu. A survey of qualitative spatial representations. Knowl. Eng. Rev., 30(1):106-136, 2015. URL: https://doi.org/10.1017/S0269888913000350.
  93. Amit Chopra, Leendert van der Torre, Harko Verhagen, and Serena Villata. Handbook of normative multiagent systems. College Publications, 2018. Google Scholar
  94. Karel Chvalovský, Konstantin Korovin, Jelle Piepenbrock, and Josef Urban. Guiding an instantiation prover with graph neural networks. In Ruzica Piskac and Andrei Voronkov, editors, Proceedings of 24th International Conference on Logic for Programming, Artificial Intelligence and Reasoning, volume 94 of EPiC Series in Computing, pages 112-123. EasyChair, 2023. URL: https://doi.org/10.29007/tp23.
  95. Claudio Di Ciccio and Marco Montali. Declarative process specifications: Reasoning, discovery, monitoring. In Wil M. P. van der Aalst and Josep Carmona, editors, Process Mining Handbook, volume 448 of Lecture Notes in Business Information Processing, pages 108-152. Springer, 2022. URL: https://doi.org/10.1007/978-3-031-08848-3_4.
  96. Jens Claßen and James P. Delgrande. An account of intensional and extensional actions, and its application to belief, nondeterministic actions and fallible sensors. In Meghyn Bienvenu, Gerhard Lakemeyer, and Esra Erdem, editors, Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning, KR 2021, pages 194-204, 2021. URL: https://doi.org/10.24963/kr.2021/19.
  97. Jens Claßen, Gerhard Lakemeyer, and Benjamin Zarrieß. Situation calculus meets description logics. In Carsten Lutz, Uli Sattler, Cesare Tinelli, Anni-Yasmin Turhan, and Frank Wolter, editors, Description Logic, Theory Combination, and All That - Essays Dedicated to Franz Baader on the Occasion of His 60th Birthday, volume 11560 of Lecture Notes in Computer Science, pages 240-265. Springer, 2019. URL: https://doi.org/10.1007/978-3-030-22102-7_11.
  98. Jens Claßen, Gabriele Röger, Gerhard Lakemeyer, and Bernhard Nebel. Platas - integrating planning and the action language golog. Künstliche Intelligenz, 26(1):61-67, 2012. URL: https://doi.org/10.1007/s13218-011-0155-2.
  99. Oana Cocarascu, Andria Stylianou, Kristijonas Čyras, and Francesca Toni. Data-empowered argumentation for dialectically explainable predictions. In Proceedings of ECAI 2020, pages 2449-2456. IOS Press, 2020. URL: https://doi.org/10.3233/FAIA200377.
  100. Roi Cohen, Mor Geva, Jonathan Berant, and Amir Globerson. Crawling the internal knowledge-base of language models. CoRR, abs/2301.12810, 2023. URL: https://doi.org/10.48550/arXiv.2301.12810.
  101. Anthony G. Cohn and Jochen Renz. Qualitative spatial representation and reasoning. In Frank van Harmelen, Vladimir Lifschitz, and Bruce W. Porter, editors, Handbook of Knowledge Representation, volume 3 of Foundations of Artificial Intelligence, pages 551-596. Elsevier, 2008. URL: https://doi.org/10.1016/S1574-6526(07)03013-1.
  102. Alain Colmerauer and Philippe Roussel. The Birth of Prolog. In John A. N. Lee and Jean E. Sammet, editors, History of Programming Languages Conference, HOPL-II, pages 37-52. ACM, 1993. URL: https://doi.org/10.1145/154766.155362.
  103. Roberto Confalonieri, Oliver Kutz, and Diego Calvanese, editors. Proceedings of the Workshop on Data meets Applied Ontologies in Explainable AI (DAO-XAI 2021) part of Bratislava Knowledge September (BAKS 2021), Bratislava, Slovakia, September 18th to 19th, 2021, volume 2998 of CEUR Workshop Proceedings. CEUR-WS.org, 2021. URL: https://ceur-ws.org/Vol-2998.
  104. Andrew Cropper and Sebastijan Dumančić. Inductive logic programming at 30: A new introduction. Journal of Artificial Intelligence Research, 74, sep 2022. URL: https://doi.org/10.1613/jair.1.13507.
  105. Zhenhe Cui, Yongmei Liu, and Kailun Luo. A uniform abstraction framework for generalized planning. In Zhi-Hua Zhou, editor, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event, pages 1837-1844. ijcai.org, 2021. URL: https://doi.org/10.24963/ijcai.2021/253.
  106. Kristijonas Čyras, Tiago Oliveira, Amin Karamlou, and Francesca Toni. Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines. Argument & Computation, 12(2):149-189, 2021. URL: https://doi.org/10.3233/AAC-200523.
  107. Kristijonas Čyras, Antonio Rago, Emanuele Albini, Pietro Baroni, and Francesca Toni. Argumentative xai: a survey. arXiv preprint arXiv:2105.11266, 2021. URL: https://doi.org/10.48550/arXiv.2105.11266.
  108. Kristijonas Cyras, Antonio Rago, Emanuele Albini, Pietro Baroni, and Francesca Toni. Argumentative XAI: A survey. In Zhi-Hua Zhou, editor, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event, pages 4392-4399. ijcai.org, 2021. URL: https://doi.org/10.24963/ijcai.2021/600.
  109. Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, and Peter Clark. Explaining answers with entailment trees. In Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih, editors, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event, pages 7358-7370. Association for Computational Linguistics, 2021. URL: https://doi.org/10.18653/v1/2021.emnlp-main.585.
  110. Adnan Darwiche. Bayesian Networks. In Frank Van Harmelen, Vladimir Lifschitz, and Bruce Porter, editors, Handbook of Knowledge Representation, pages 467-509. Elsevier, 2008. URL: https://doi.org/10.1016/S1574-6526(07)03011-8.
  111. Adnan Darwiche and Judea Pearl. On the Logic of Iterated Belief Revision. Artificial intelligence, 89(1-2):1-29, 1997. URL: https://doi.org/10.1016/S0004-3702(96)00038-0.
  112. Ernest Davis. Physical reasoning. In Frank van Harmelen, Vladimir Lifschitz, and Bruce W. Porter, editors, Handbook of Knowledge Representation, volume 3 of Foundations of Artificial Intelligence, pages 597-620. Elsevier, 2008. URL: https://doi.org/10.1016/S1574-6526(07)03014-3.
  113. Ernest Davis. Logical formalizations of commonsense reasoning: A survey. Journal of Artificial Intelligence Research, 59:651-723, 2017. URL: https://doi.org/10.1613/jair.5339.
  114. Ernest Davis and Gary Marcus. Commonsense reasoning and commonsense knowledge in artificial intelligence. Commun. ACM, 58(9):92-103, aug 2015. URL: https://doi.org/10.1145/2701413.
  115. Randall Davis, Bruce Buchanan, and Edward Shortliffe. Production Rules as a Representation for a Knowledge-Based Consultation Program. Artificial intelligence, 8(1):15-45, 1977. URL: https://doi.org/10.1016/0004-3702(77)90003-0.
  116. Giuseppe De Giacomo, Riccardo De Masellis, Fabrizio Maria Maggi, and Marco Montali. Monitoring constraints and metaconstraints with temporal logics on finite traces. ACM Transactions on Software Engineering Methodologies, 31(4), jul 2022. URL: https://doi.org/10.1145/3506799.
  117. Giuseppe De Giacomo and Sasha Rubin. Automata-Theoretic Foundations of FOND Planning for LTLf and LDLf Goals. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI'18, pages 4729-4735. AAAI Press, 2018. URL: https://doi.org/10.24963/ijcai.2018/657.
  118. Johan de Kleer, Alan Mackworth, and Raymond Reiter. Characterizing diagnoses and systems. Journal of Artificial Intelligence Research, 52(2-3):197-222, 1992. URL: https://doi.org/10.1016/0004-3702(92)90027-U.
  119. James P. Delgrande. What’s in a default? Thoughts on the nature and role of defaults in nonmonotonic reasoning. In Gerhard Brewka, Victor W. Marek, and Miroslaw Truszczynski, editors, Nonmonotonic Reasoning: Essays Celebrating its 30th Anniversary. College Publications, 2011. Google Scholar
  120. James P. Delgrande and P. Peppas. Belief revision in Horn theories. Artificial Intelligence Journal, 218:1-22, 2015. URL: https://doi.org/10.1016/j.artint.2014.08.006.
  121. James P. Delgrande, Pavlos Peppas, and Stefan Woltran. General belief revision. Journal of the Association of Computing Machinery, 65(5):1-34, 2018. URL: https://doi.org/10.1145/3203409.
  122. James P. Delgrande and R. Wassermann. Horn clause contraction functions. Journal of Artificial Intelligence Research, 48:475-511, nov 2013. URL: https://doi.org/10.1613/jair.4031.
  123. Thomas Demeester, Tim Rocktäschel, and Sebastian Riedel. Lifted rule injection for relation embeddings. In Jian Su, Xavier Carreras, and Kevin Duh, editors, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, pages 1389-1399. The Association for Computational Linguistics, 2016. URL: https://doi.org/10.18653/v1/d16-1146.
  124. Marc Denecker, Victor W. Marek, and Miroslaw Truszczyński. Uniform semantic treatment of default and autoepistemic logics. Artificial Intelligence Journal, 143(1):79-122, 2003. URL: https://doi.org/10.1016/S0004-3702(02)00293-X.
  125. Alin Deutsch, Richard Hull, Yuliang Li, and Victor Vianu. Automatic verification of database-centric systems. ACM SIGLOG News, 5(2):37-56, 2018. URL: https://doi.org/10.1145/3212019.3212025.
  126. DLV project, 2017+. URL: https://dlv.demacs.unical.it/home.
  127. Carmine Dodaro, Philip Gasteiger, Kristian Reale, Francesco Ricca, and Konstantin Schekotihin. Debugging non-ground ASP programs: Technique and graphical tools. Theory and Practice of Logic Programming, 19(2):290-316, 2019. URL: https://doi.org/10.1017/S1471068418000492.
  128. A. Doxiadis and C.H. Papadimitriou. Logicomix. Bloomsbury, New York, US, 2009. Google Scholar
  129. Richard Duda, John Gaschnig, and Peter Hart. Model Design in the PROSPECTOR Consultant System for Mineral Exploration. In Readings in Artificial Intelligence, pages 334-348. Elsevier, 1981. Google Scholar
  130. Phan Minh Dung. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence, 77(2):321-358, 1995. URL: https://doi.org/10.1016/0004-3702(94)00041-X.
  131. Wolfgang Dvorák, Alexander Greßler, Anna Rapberger, and Stefan Woltran. The complexity landscape of claim-augmented argumentation frameworks. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, pages 6296-6303. AAAI Press, 2021. URL: https://doi.org/10.1609/AAAI.V35I7.16782.
  132. Thomas Eiter and Georg Gottlob. Propositional circumscription and extended closed-world reasoning are π₂^p-complete. Theoretical Computer Science, 114(2):231-245, 1993. URL: https://doi.org/10.1016/0304-3975(93)90073-3.
  133. Esra Erdem, Muge Fidan, David F. Manlove, and Patrick Prosser. A general framework for stable roommates problems using answer set programming. Theory and Practice of Logic Programming, 20(6):911-925, 2020. URL: https://doi.org/10.1017/S1471068420000277.
  134. Esra Erdem, Michael Gelfond, and Nicola Leone. Applications of answer set programming. AI Mag., 37(3):53-68, 2016. URL: https://doi.org/10.1609/aimag.v37i3.2678.
  135. Richard Evans and Edward Grefenstette. Learning explanatory rules from noisy data. Journal of Artificial Intelligence Research, 61:1-64, 2018. URL: https://doi.org/10.1613/jair.5714.
  136. Patrick Eyerich, Robert Mattmüller, and Gabriele Röger. Using the context-enhanced additive heuristic for temporal and numeric planning. In Erwin Prassler, Johann Marius Zöllner, Rainer Bischoff, Wolfram Burgard, Robert Haschke, Martin Hägele, Gisbert Lawitzky, Bernhard Nebel, Paul-Gerhard Plöger, and Ulrich Reiser, editors, Towards Service Robots for Everyday Environments - Recent Advances in Designing Service Robots for Complex Tasks in Everyday Environments, volume 76 of Springer Tracts in Advanced Robotics, pages 49-64. Springer, 2012. URL: https://doi.org/10.1007/978-3-642-25116-0_6.
  137. Ronald Fagin, Phokion G. Kolaitis, Renée J. Miller, and Lucian Popa. Data exchange: semantics and query answering. Theoretical Computer Science, 336(1):89-124, 2005. URL: https://doi.org/10.1016/j.tcs.2004.10.033.
  138. Andreas A. Falkner, Gerhard Friedrich, Konstantin Schekotihin, Richard Taupe, and Erich Christian Teppan. Industrial applications of answer set programming. Künstliche Intelligenz, 32(2-3):165-176, 2018. URL: https://doi.org/10.1007/s13218-018-0548-6.
  139. Jorge Fandinno and Markus Hecher. Treewidth-aware complexity in ASP: not all positive cycles are equally hard. In Thirty-Fifth AAAI Conference on Artificial Intelligence, pages 6312-6320. AAAI Press, 2021. URL: https://ojs.aaai.org/index.php/AAAI/article/view/16784, URL: https://doi.org/10.1609/AAAI.V35I7.16784.
  140. Onofrio Febbraro, Kristian Reale, and Francesco Ricca. ASPIDE: integrated development environment for answer set programming. In James P. Delgrande and Wolfgang Faber, editors, Logic Programming and Nonmonotonic Reasoning - 11th International Conference, LPNMR 2011, volume 6645 of Lecture Notes in Computer Science, pages 317-330. Springer, 2011. URL: https://doi.org/10.1007/978-3-642-20895-9_37.
  141. Paolo Felli, Alessandro Gianola, Marco Montali, Andrey Rivkin, and Sarah Winkler. Cocomot: Conformance checking of multi-perspective processes via SMT. In Artem Polyvyanyy, Moe Thandar Wynn, Amy Van Looy, and Manfred Reichert, editors, Business Process Management - 19th International Conference, BPM 2021, volume 12875 of Lecture Notes in Computer Science, pages 217-234. Springer, 2021. URL: https://doi.org/10.1007/978-3-030-85469-0_15.
  142. Johannes Klaus Fichte, Markus Hecher, and Mohamed A. Nadeem. Plausibility reasoning via projected answer set counting - A hybrid approach. In Luc De Raedt, editor, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, pages 2620-2626. ijcai.org, 2022. URL: https://doi.org/10.24963/ijcai.2022/363.
  143. Nathanaël Fijalkow, Bastien Maubert, Aniello Murano, Sasha Rubin, and Moshe Vardi. Public and Private Affairs in Strategic Reasoning. In Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning, pages 132-140, aug 2022. URL: https://doi.org/10.24963/kr.2022/14.
  144. Richard E. Fikes and Nils J. Nilsson. STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving. Artificial intelligence, 2(3-4):189-208, 1971. URL: https://doi.org/10.1016/0004-3702(71)90010-5.
  145. Michael Fisher. Temporal representation and reasoning. In Frank van Harmelen, Vladimir Lifschitz, and Bruce W. Porter, editors, Handbook of Knowledge Representation, volume 3 of Foundations of Artificial Intelligence, pages 513-550. Elsevier, 2008. URL: https://doi.org/10.1016/S1574-6526(07)03012-X.
  146. Kenneth D. Forbus. Qualitative modeling. In Frank van Harmelen, Vladimir Lifschitz, and Bruce W. Porter, editors, Handbook of Knowledge Representation, volume 3 of Foundations of Artificial Intelligence, pages 361-393. Elsevier, 2008. URL: https://doi.org/10.1016/S1574-6526(07)03009-X.
  147. Enrico Franconi and Gary Ng. The i.com tool for intelligent conceptual modeling. In Proceedings of the 7th International Workshop on Knowledge Representation meets Databases (KRDB 2000), pages 45-53, 2000. URL: https://ceur-ws.org/Vol-29/04-franconi.ps.
  148. Dov Gabbay, John Horty, Xavier Parent, Ron van der Meyden, and Leendert van der Torre, editors. Handbook of Deontic Logic and Normative Systems. College Publications, 2013. Google Scholar
  149. Dov Gabbay, Agi Kurucz, Frank Wolter, and Michael Zakharyachev. Many-dimensional modal logics: theory and applications. Studies in Logic and the Foundations of Mathematics. Elsevier, 2003. Google Scholar
  150. Krzysztof Gajowniczek, Yitao Liang, Tal Friedman, Tomasz Zabkowski, and Guy Van den Broeck. Semantic and generalized entropy loss functions for semi-supervised deep learning. Entropy, 22(3):334, 2020. URL: https://doi.org/10.3390/e22030334.
  151. P. Gärdenfors and D. Makinson. Nonmonotonic inference based on expectations. Artificial Intelligence, 65(2):197-245, 1994. URL: https://doi.org/10.1016/0004-3702(94)90017-5.
  152. Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, and Tomás Lozano-Pérez. Integrated task and motion planning. CoRR, abs/2010.01083, 2020. URL: https://doi.org/10.48550/arXiv.2010.01083.
  153. Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub. Answer Set Programming in Practice. Springer, 2022. Google Scholar
  154. Hector Geffner. Target languages (vs. inductive biases) for learning to act and plan. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, pages 12326-12333. AAAI Press, 2022. URL: https://doi.org/10.1609/aaai.v36i11.21497.
  155. Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann. Shortcut learning in deep neural networks. Nat. Mach. Intell., 2(11):665-673, 2020. URL: https://doi.org/10.1038/s42256-020-00257-z.
  156. Michael Gelfond and Vladimir Lifschitz. The stable model semantics for logic programming. In Robert A. Kowalski and Kenneth A. Bowen, editors, Logic Programming, Proceedings of the Fifth International Conference and Symposium, pages 1070-1080. MIT Press, 1988. Google Scholar
  157. Michael Gelfond and Vladimir Lifschitz. Representing Action and Change by Logic Programs. The Journal of Logic Programming, 17(2-4):301-321, 1993. URL: https://doi.org/10.1016/0743-1066(93)90035-F.
  158. Lise Getoor and Ben Taskar, editors. Introduction to Statistical Relational Learning. Adaptive Computation and Machine Learning. MIT Press, 2007. Google Scholar
  159. Malik Ghallab and Hervé Laruelle. Representation and control in ixtet, a temporal planner. In Proceedings of the Second International Conference on Artificial Intelligence Planning Systems, pages 61-67, 1994. URL: http://www.aaai.org/Library/AIPS/1994/aips94-011.php.
  160. Giuseppe De Giacomo, Paolo Felli, Brian Logan, Fabio Patrizi, and Sebastian Sardiña. Situation calculus for controller synthesis in manufacturing systems with first-order state representation. Artificial Intelligence, 302:103598, 2022. URL: https://doi.org/10.1016/j.artint.2021.103598.
  161. Giuseppe De Giacomo, Luca Iocchi, Marco Favorito, and Fabio Patrizi. Restraining bolts for reinforcement learning agents. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, pages 13659-13662. AAAI Press, 2020. URL: https://doi.org/10.1609/aaai.v34i09.7114.
  162. Giuseppe De Giacomo, Domenico Lembo, Maurizio Lenzerini, Antonella Poggi, and Riccardo Rosati. Using ontologies for semantic data integration. In Sergio Flesca, Sergio Greco, Elio Masciari, and Domenico Saccà, editors, A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, volume 31 of Studies in Big Data, pages 187-202. Springer International Publishing, 2018. URL: https://doi.org/10.1007/978-3-319-61893-7_11.
  163. Giuseppe De Giacomo and Yves Lespérance. Goal formation through interaction in the situation calculus: A formal account grounded in behavioral science. In Amal El Fallah Seghrouchni, Gita Sukthankar, Bo An, and Neil Yorke-Smith, editors, Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS '20, pages 294-302. International Foundation for Autonomous Agents and Multiagent Systems, 2020. URL: https://doi.org/10.5555/3398761.3398800.
  164. Giuseppe De Giacomo, Yves Lespérance, and Fabio Patrizi. Bounded situation calculus action theories. Artificial Intelligence, 237:172-203, 2016. URL: https://doi.org/10.1016/j.artint.2016.04.006.
  165. Giuseppe De Giacomo, Yves Lespérance, and Eugenia Ternovska. Elgolog: A high-level programming language with memory of the execution history. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, pages 2806-2813. AAAI Press, 2020. URL: https://doi.org/10.1609/AAAI.V34I03.5669.
  166. Giuseppe De Giacomo, Fabrizio Maria Maggi, Andrea Marrella, and Fabio Patrizi. On the disruptive effectiveness of automated planning for LTLf-based trace alignment. In Satinder Singh and Shaul Markovitch, editors, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pages 3555-3561. AAAI Press, 2017. URL: https://doi.org/10.1609/AAAI.V31I1.11020.
  167. Giuseppe De Giacomo, Raymond Reiter, and Mikhail Soutchanski. Execution monitoring of high-level robot programs. In Anthony G. Cohn, Lenhart K. Schubert, and Stuart C. Shapiro, editors, Proceedings of the Sixth International Conference on Principles of Knowledge Representation and Reasoning (KR'98), pages 453-465. Morgan Kaufmann, 1998. Google Scholar
  168. Giuseppe De Giacomo and Moshe Y. Vardi. Linear temporal logic and linear dynamic logic on finite traces. In Francesca Rossi, editor, IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, pages 854-860. IJCAI/AAAI, 2013. Google Scholar
  169. Giuseppe De Giacomo and Moshe Y. Vardi. Synthesis for LTL and LDL on finite traces. In Qiang Yang and Michael J. Wooldridge, editors, Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, pages 1558-1564. AAAI Press, 2015. URL: http://ijcai.org/Abstract/15/223.
  170. L. Giordano, V. Gliozzi, N. Olivetti, and G.L. Pozzato. Semantic characterization of rational closure: From propositional logic to description logics. Artificial Intelligence, 226:1-33, 2015. URL: https://doi.org/10.1016/j.artint.2015.05.001.
  171. Birte Glimm, Ian Horrocks, Boris Motik, Giorgos Stoilos, and Zhe Wang. Hermit: An OWL 2 reasoner. Journal of Automated Reasoning, 2014. submitted. URL: https://doi.org/10.1007/s10817-014-9305-1.
  172. Birte Glimm and Yevgeny Kazakov. Classical algorithms for reasoning and explanation in description logics. In Markus Krötzsch and Daria Stepanova, editors, Reasoning Web. Explainable Artificial Intelligence - 15th International Summer School 2019, volume 11810 of Lecture Notes in Computer Science, pages 1-64. Springer, 2019. URL: https://doi.org/10.1007/978-3-030-31423-1_1.
  173. Pierpaolo Goffredo, Shohreh Haddadan, Vorakit Vorakitphan, Elena Cabrio, and Serena Villata. Fallacious argument classification in political debates. In Proceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022, pages 4143-4149, 2022. URL: https://doi.org/10.24963/ijcai.2022/575.
  174. Nikos Gorogiannis and Anthony Hunter. Instantiating abstract argumentation with classical logic arguments: Postulates and properties. Artificial Intelligence, 175(9-10):1479-1497, 2011. URL: https://doi.org/10.1016/j.artint.2010.12.003.
  175. Georg Gottlob. Complexity results for nonmonotonic logics. Journal of Logic and Computation, 2(3):397-425, 1992. URL: https://doi.org/10.1093/logcom/2.3.397.
  176. Henrik Grosskreutz and Gerhard Lakemeyer. Probabilistic complex actions in golog. Fundamenta Informaticae, 57(2-4):167-192, 2003. Google Scholar
  177. Susana Hahn, Orkunt Sabuncu, Torsten Schaub, and Tobias Stolzmann. Clingraph: ASP-based visualization. In Georg Gottlob, Daniela Inclezan, and Marco Maratea, editors, Logic Programming and Nonmonotonic Reasoning - 16th International Conference, LPNMR 2022, volume 13416 of Lecture Notes in Computer Science, pages 401-414. Springer, 2022. URL: https://doi.org/10.1007/978-3-031-15707-3_31.
  178. Shai Haim and Toby Walsh. Restart strategy selection using machine learning techniques. In Oliver Kullmann, editor, Theory and Applications of Satisfiability Testing - SAT 2009, 12th International Conference, SAT 2009, volume 5584 of Lecture Notes in Computer Science, pages 312-325. Springer, 2009. URL: https://doi.org/10.1007/978-3-642-02777-2_30.
  179. Joseph Y. Halpern. An analysis of first-order logics of probability. Artificial Intelligence, 46(3):311-350, 1990. URL: https://doi.org/10.1016/0004-3702(90)90019-V.
  180. Shibo Hao, Bowen Tan, Kaiwen Tang, Hengzhe Zhang, Eric P. Xing, and Zhiting Hu. Bertnet: Harvesting knowledge graphs from pretrained language models. CoRR, abs/2206.14268, 2022. URL: https://doi.org/10.48550/arXiv.2206.14268.
  181. Babak Bagheri Hariri, Diego Calvanese, Marco Montali, Giuseppe De Giacomo, Riccardo De Masellis, and Paolo Felli. Description logic knowledge and action bases. Journal of Artificial Intelligence Research, 46:651-686, 2013. URL: https://doi.org/10.1613/jair.3826.
  182. Amelia J. Harrison, Vladimir Lifschitz, and Fangkai Yang. The semantics of gringo and infinitary propositional formulas. In Chitta Baral, Giuseppe De Giacomo, and Thomas Eiter, editors, Principles of Knowledge Representation and Reasoning: Proceedings of the Fourteenth International Conference, KR 2014. AAAI Press, 2014. URL: http://www.aaai.org/ocs/index.php/KR/KR14/paper/view/7966.
  183. Patrick J. Hayes and Peter F. Patel-Schneider, editors. RDF 1.1 Semantics. W3C Recommendation, 25 February 2014. Available at URL: https://www.w3.org/TR/rdf11-mt/.
  184. P.J. Hayes. In defense of logic. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 559-565, Cambridge, MA, 1977. URL: http://ijcai.org/Proceedings/77-1/Papers/099.pdf.
  185. P.J. Hayes. The logic of frames. In D. Metzing, editor, Frame Conceptions and Text Understanding, pages 46-61. Walter de Gruyter and Co., 1979. Google Scholar
  186. P.J. Hayes. Naive physics I: Ontology for liquids. In J.R. Hobbs and R.C. Moore, editors, Formal Theories of the Commonsense World, pages 71-108. Ablex, 1985. Google Scholar
  187. P.J. Hayes. The second naive physics manifesto. In J.R. Hobbs and R.C. Moore, editors, Formal Theories of the Commonsense World, pages 1-36. Ablex, 1985. Google Scholar
  188. Markus Hecher. Treewidth-aware reductions of normal ASP to SAT - is normal ASP harder than SAT after all? Artificial Intelligence, 304:103651, 2022. URL: https://doi.org/10.1016/j.artint.2021.103651.
  189. Fredrik Heintz, Gerhard Lakemeyer, and Sheila McIlraith. Cognitive Robotics (Dagstuhl Seminar 22391). Dagstuhl Reports, 12(9):200-219, 2023. URL: https://doi.org/10.4230/DagRep.12.9.200.
  190. C. Hewitt. Planner: A language for proving theorems in robots. In International Joint Conference on Artificial Intelligence, London, U.K., 1971. Google Scholar
  191. Pascal Hitzler and Md. Kamruzzaman Sarker, editors. Neuro-Symbolic Artificial Intelligence: The State of the Art, volume 342 of Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. URL: https://doi.org/10.3233/FAIA342.
  192. Nick Hoernle, Rafael Michael Karampatsis, Vaishak Belle, and Kobi Gal. Multiplexnet: Towards fully satisfied logical constraints in neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 5700-5709, 2022. URL: https://doi.org/10.1609/aaai.v36i5.20512.
  193. Jörg Hoffmann and Bernhard Nebel. The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research, 14:253-302, 2001. URL: https://doi.org/10.1613/jair.855.
  194. Till Hofmann, Tim Niemueller, Jens Claßen, and Gerhard Lakemeyer. Continual planning in golog. In Dale Schuurmans and Michael P. Wellman, editors, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pages 3346-3353. AAAI Press, 2016. URL: https://doi.org/10.1609/AAAI.V30I1.10414.
  195. Till Hofmann, Tarik Viehmann, Mostafa Gomaa, Daniel Habering, Tim Niemueller, and Gerhard Lakemeyer. Multi-agent goal reasoning with the CLIPS executive in the robocup logistics league. In Ana Paula Rocha, Luc Steels, and H. Jaap van den Herik, editors, Proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, pages 80-91. SCITEPRESS, 2021. URL: https://doi.org/10.5220/0010252600800091.
  196. Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia D’amato, Gerard De Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann. Knowledge graphs. ACM Computing Surveys, 54(4), jul 2021. URL: https://doi.org/10.1145/3447772.
  197. Matthew Horridge. Justification based explanation in ontologies. PhD thesis, University of Manchester, UK, 2011. URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:131699.
  198. Ian Horrocks. DAML+OIL: A description logic for the semantic web. IEEE Data Eng. Bull., 25(1):4-9, 2002. URL: http://sites.computer.org/debull/A02MAR-CD.pdf.
  199. John Horty. Epistemic oughts in stit semantics. Ergo, an Open Access Journal of Philosophy, 6, 2019. Google Scholar
  200. G.E. Hughes and M.J. Cresswell. An Introduction to Modal Logic. Methuen and Co. Ltd., 1968. Google Scholar
  201. Anthony Hunter. Towards a framework for computational persuasion with applications in behaviour change. Argument & Computation, 9(1):15-40, 2018. URL: https://doi.org/10.3233/AAC-170032.
  202. Anthony Hunter, Gabriele Kern-Isberner, Thomas Meyer, and Renata Wassermann. The role of non-monotonic reasoning in future development of artificial intelligence (Dagstuhl Perspectives workshop 19072). Dagstuhl Reports, 9(2):73-90, 2019. URL: https://doi.org/10.4230/DagRep.9.2.73.
  203. Dieuwke Hupkes, Verna Dankers, Mathijs Mul, and Elia Bruni. Compositionality decomposed: How do neural networks generalise? Journal of Artificial Intelligence Research, 67:757-795, 2020. URL: https://doi.org/10.1613/jair.1.11674.
  204. Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, and Yejin Choi. COMET-ATOMIC 2020: On symbolic and neural commonsense knowledge graphs. CoRR, abs/2010.05953, 2020. https://arxiv.org/abs/2010.05953, URL: https://doi.org/10.48550/arXiv.2010.05953.
  205. Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Anthony Valenzano, and Sheila A. McIlraith. Reward machines: Exploiting reward function structure in reinforcement learning. Journal of Artificial Intelligence Research, 73:173-208, 2022. URL: https://doi.org/10.1613/jair.1.12440.
  206. Benjamin Irwin, Antonio Rago, and Francesca Toni. Forecasting argumentation frameworks. arXiv preprint arXiv:2205.11590, 2022. URL: https://doi.org/10.48550/arXiv.2205.11590.
  207. Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and Philip S. Yu. A Survey on Knowledge Graphs: Representation, Acquisition, and Applications. IEEE Transactions on Neural Networks and Learning Systems, 33(2):494-514, 2022. URL: https://doi.org/10.1109/TNNLS.2021.3070843.
  208. Kai Jia and Martin Rinard. Efficient exact verification of binarized neural networks. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS'20. Curran Associates Inc., 2020. URL: https://proceedings.neurips.cc/paper/2020/hash/1385974ed5904a438616ff7bdb3f7439-Abstract.html.
  209. E. Jiménez Ruiz, B. Cuenca Grau, I. Horrocks, and R. Berlanga. Supporting concurrent ontology development: Framework, algorithms and tool. Data and Knowledge Engineering, 70(1):146-164, 2011. URL: https://doi.org/10.1016/j.datak.2010.10.001.
  210. Y. Jin and M. Thielscher. Iterated belief revision, revised. Artificial Intelligence, 171(1):1-18, 2007. URL: https://doi.org/10.1016/j.artint.2006.11.002.
  211. Zijian Jin, Xingyu Zhang, Mo Yu, and Lifu Huang. Probing script knowledge from pre-trained models. CoRR, abs/2204.10176, 2022. URL: https://doi.org/10.48550/arXiv.2204.10176.
  212. Jean Christoph Jung, Carsten Lutz, Hadrien Pulcini, and Frank Wolter. Logical separability of labeled data examples under ontologies. Artificial Intelligence, 313:103785, 2022. URL: https://doi.org/10.1016/j.artint.2022.103785.
  213. Mohimenul Kabir, Flavio O. Everardo, Ankit K. Shukla, Markus Hecher, Johannes Klaus Fichte, and Kuldeep S. Meel. ApproxASP - a scalable approximate answer set counter. In Thirty-Sixth AAAI Conference on Artificial Intelligence, pages 5755-5764. AAAI Press, 2022. URL: https://doi.org/10.1609/AAAI.V36I5.20518.
  214. Roland Kaminski, Javier Romero, Torsten Schaub, and Philipp Wanko. How to build your own ASP-based system?! CoRR, abs/2008.06692, 2020. URL: https://doi.org/10.48550/arXiv.2008.06692.
  215. Roland Kaminski and Torsten Schaub. On the foundations of grounding in answer set programming. CoRR, abs/2108.04769, 2021. URL: https://doi.org/10.48550/arXiv.2108.04769.
  216. H. Katsuno and A. Mendelzon. On the difference between updating a knowledge base and revising it. In P. Gärdenfors, editor, Belief Revision, pages 183-203, Cambridge, 1992. Cambridge University Press. Google Scholar
  217. Yevgeny Kazakov, Markus Krötzsch, and František Simančík. The incredible ELK: From polynomial procedures to efficient reasoning with EL ontologies. Journal of Automated Reasoning, 53(1):1-61, 2014. URL: https://doi.org/10.1007/s10817-013-9296-3.
  218. K. Kersting and L. De Raedt. Bayesian logic programs. In ILP Work-in-progress reports, 2000. URL: https://ceur-ws.org/Vol-35/07-KerstingDeRaedt.ps.
  219. Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. Concept bottleneck models. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pages 5338-5348. PMLR, 2020. URL: http://proceedings.mlr.press/v119/koh20a.html.
  220. Stanley Kok and Pedro Domingos. Learning markov logic networks using structural motifs. In ICML, pages 551-558, 2010. URL: https://icml.cc/Conferences/2010/papers/502.pdf.
  221. D. Koller, A. Levy, and A. Pfeffer. P-classic: a tractable probablistic description logic. In Proc. AAAI / IAAI, pages 390-397, 1997. URL: http://www.aaai.org/Library/AAAI/1997/aaai97-060.php.
  222. Boris Konev, Dirk Walther, and Frank Wolter. Forgetting and uniform interpolation in large-scale description logic terminologies. In Craig Boutilier, editor, IJCAI, pages 830-835, 2009. URL: http://ijcai.org/Proceedings/09/Papers/142.pdf.
  223. S. Konieczny and R. Pino Pérez. Merging information under constraints: A logical framework. Journal of Logic and Computation, 12(5):773-808, 2002. URL: https://doi.org/10.1093/logcom/12.5.773.
  224. Sébastien Konieczny. On the Difference between Merging Knowledge Bases and Combining them. In Anthony G. Cohn, Fausto Giunchiglia, and Bart Selman, editors, Proceedings od the 7th International Conference on Principles of Knowledge Representation and Reasoning, KR 2000, pages 135-144. Morgan Kaufmann, 2000. Google Scholar
  225. Robert Kowalski. Predicate Logic as Programming Language. In IFIP congress, volume 74, pages 569-544, 1974. Google Scholar
  226. Robert Kowalski and Donald Kuehner. Linear Resolution with Selection Function. Artificial Intelligence, 2(3-4):227-260, 1971. URL: https://doi.org/10.1016/0004-3702(71)90012-9.
  227. Robert A. Kowalski and Marek J. Sergot. A Logic-based Calculus of Events. New Gener. Comput., 4(1):67-95, 1986. URL: https://doi.org/10.1007/BF03037383.
  228. D. Kozen. A probabilistic PDLL. Journal of Computer and System Sciences, 30(2):162-178, 1985. URL: https://doi.org/10.1016/0022-0000(85)90012-1.
  229. S. Kraus, D. Lehmann, and M. Magidor. Nonmonotonic reasoning, preferential models and cumulative logics. Artificial Intelligence, 44(1-2):167-207, 1990. URL: https://doi.org/10.1016/0004-3702(90)90101-5.
  230. S.A. Kripke. Naming and Necessity. Harvard University Press, Cambridge, Mass., 1980. Google Scholar
  231. Markus Krötzsch, Maximilian Marx, Ana Ozaki, and Veronika Thost. Attributed description logics: Reasoning on knowledge graphs. In Jérôme Lang, editor, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, pages 5309-5313. ijcai.org, 2018. URL: https://doi.org/10.24963/ijcai.2018/743.
  232. Markus Krötzsch, František Simančík, and Ian Horrocks. Description logics. IEEE Intelligent Systems, 29(1):12-19, 2014. URL: https://doi.org/10.1109/MIS.2013.123.
  233. Benjamin Kuipers, Edward Feigenbaum, Peter E. Hart, and Nils J. Nilsson. Shakey: from conception to history. AI Magazine, 38(1):88-103, 2017. URL: https://doi.org/10.1609/aimag.v38i1.2716.
  234. Ondrej Kuzelka, Jesse Davis, and Steven Schockaert. Learning possibilistic logic theories from default rules. In Subbarao Kambhampati, editor, Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, pages 1167-1173. IJCAI/AAAI Press, 2016. URL: http://www.ijcai.org/Abstract/16/169.
  235. Jonas Kvarnström and Patrick Doherty. Talplanner: A temporal logic based forward chaining planner. Annals of Mathematics and Artificial Intelligence, 30(1-4):119-169, 2000. URL: https://doi.org/10.1023/A:1016619613658.
  236. B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. Human-level concept learning through probabilistic program induction. Science, 350(6266):1332-1338, 2015. URL: https://doi.org/10.1126/science.aab3050.
  237. Thomas Léauté and Brian C. Williams. Coordinating agile systems through the model-based execution of temporal plans. In Proceedings, The Twentieth National Conference on Artificial Intelligence, pages 114-120, 2005. URL: http://www.aaai.org/Library/AAAI/2005/aaai05-019.php.
  238. Joohyung Lee and Zhun Yang. LPMLN, weak constraints, and p-log. In Satinder Singh and Shaul Markovitch, editors, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pages 1170-1177. AAAI Press, 2017. URL: https://doi.org/10.1609/aaai.v31i1.10686.
  239. Kevin Lee, Thomas Andreas Meyer, Jeff Z. Pan, and Richard Booth. Computing maximally satisfiable terminologies for the description logic ALC with cyclic definitions. In Bijan Parsia, Ulrike Sattler, and David Toman, editors, Proceedings of the 2006 International Workshop on Description Logics (DL2006), volume 189 of CEUR Workshop Proceedings. CEUR-WS.org, 2006. URL: https://ceur-ws.org/Vol-189/submission_29.pdf.
  240. Daniel Lehmann and Menachem Magidor. What does a conditional knowledge base entail? Artificial Intelligence, 55:1-60, 1992. URL: https://doi.org/10.1016/0004-3702(92)90041-U.
  241. Tuomo Lehtonen, Johannes P Wallner, and Matti Järvisalo. Declarative algorithms and complexity results for assumption-based argumentation. Journal of Artificial Intelligence Research, 71:265-318, 2021. URL: https://doi.org/10.1613/jair.1.12479.
  242. Maurizio Lenzerini. Data integration: A theoretical perspective. In Proceedings of the Twenty-first ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pages 233-246, 2002. URL: https://doi.org/10.1145/543613.543644.
  243. Hector J. Levesque and Gerhard Lakemeyer. Cognitive robotics. In Frank van Harmelen, Vladimir Lifschitz, and Bruce W. Porter, editors, Handbook of Knowledge Representation, volume 3 of Foundations of Artificial Intelligence, pages 869-886. Elsevier, 2008. URL: https://doi.org/10.1016/S1574-6526(07)03023-4.
  244. Hector J. Levesque, Raymond Reiter, Yves Lespérance, Fangzhen Lin, and Richard B. Scherl. GOLOG: A logic programming language for dynamic domains. Journal of Logic Programming, 31(1-3):59-83, 1997. URL: https://doi.org/10.1016/S0743-1066(96)00121-5.
  245. Na Li, Zied Bouraoui, and Steven Schockaert. Ontology completion using graph convolutional networks. In Chiara Ghidini, Olaf Hartig, Maria Maleshkova, Vojtech Svátek, Isabel F. Cruz, Aidan Hogan, Jie Song, Maxime Lefrançois, and Fabien Gandon, editors, The Semantic Web - ISWC 2019 - 18th International Semantic Web Conference, volume 11778 of Lecture Notes in Computer Science, pages 435-452. Springer, 2019. URL: https://doi.org/10.1007/978-3-030-30793-6_25.
  246. Oscar Li, Hao Liu, Chaofan Chen, and Cynthia Rudin. Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions. In Sheila A. McIlraith and Kilian Q. Weinberger, editors, Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), pages 3530-3537. AAAI Press, 2018. URL: https://doi.org/10.1609/AAAI.V32I1.11771.
  247. Tao Li and Vivek Srikumar. Augmenting neural networks with first-order logic. In Anna Korhonen, David R. Traum, and Lluís Màrquez, editors, Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, pages 292-302. Association for Computational Linguistics, 2019. URL: https://doi.org/10.18653/v1/p19-1028.
  248. Jia Hui Liang, Vijay Ganesh, Pascal Poupart, and Krzysztof Czarnecki. Learning rate based branching heuristic for SAT solvers. In Nadia Creignou and Daniel Le Berre, editors, Theory and Applications of Satisfiability Testing - SAT 2016 - 19th International Conference, volume 9710 of Lecture Notes in Computer Science, pages 123-140. Springer, 2016. URL: https://doi.org/10.1007/978-3-319-40970-2_9.
  249. Yuliya Lierler. Constraint answer set programming: Integrational and translational (or smt-based) approaches. CoRR, abs/2107.08252, 2021. URL: https://doi.org/10.48550/arXiv.2107.08252.
  250. Yuliya Lierler, Marco Maratea, and Francesco Ricca. Systems, engineering environments, and competitions. AI Mag., 37(3):45-52, 2016. URL: https://doi.org/10.1609/aimag.v37i3.2675.
  251. Vladimir Lifschitz. Answer Set Programming. Springer, 2019. URL: https://doi.org/10.1007/978-3-030-24658-7.
  252. Songtuan Lin, Alban Grastien, and Pascal Bercher. Towards automated modeling assistance: An efficient approach for repairing flawed planning domains. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI 2023). AAAI Press, 2023. URL: https://doi.org/10.1609/aaai.v37i10.26418.
  253. Felix Lindner, Robert Mattmüller, and Bernhard Nebel. Evaluation of the moral permissibility of action plans. Artificial Intelligence, 287, 2020. URL: https://doi.org/10.1016/j.artint.2020.103350.
  254. Daxin Liu and Gerhard Lakemeyer. Reasoning about beliefs and meta-beliefs by regression in an expressive probabilistic action logic. In Zhi-Hua Zhou, editor, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, pages 1951-1958. ijcai.org, 2021. URL: https://doi.org/10.24963/ijcai.2021/269.
  255. Daniel Lowd and Amirmohammad Rooshenas. Learning markov networks with arithmetic circuits. In AISTATS, 2013. URL: http://proceedings.mlr.press/v31/lowd13a.html.
  256. Matt Luckcuck, Marie Farrell, Louise A. Dennis, Clare Dixon, and Michael Fisher. Formal specification and verification of autonomous robotic systems: A survey. ACM Computing Surveys, 52(5):100:1-100:41, 2019. URL: https://doi.org/10.1145/3342355.
  257. Thomas Lukasiewicz. Expressive probabilistic description logics. Artificial Intelligence, 172(6):852-883, 2008. URL: https://doi.org/10.1016/j.artint.2007.10.017.
  258. Carsten Lutz, Frank Wolter, and Michael Zakharyaschev. Temporal description logics: A survey. In Stéphane Demri and Christian S. Jensen, editors, 15th International Symposium on Temporal Representation and Reasoning, TIME 2008, pages 3-14. IEEE Computer Society, 2008. URL: https://doi.org/10.1109/TIME.2008.14.
  259. Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, and Luc De Raedt. DeepProbLog: Neural Probabilistic Logic Programming. Advances in Neural Information Processing Systems, 31, 2018. URL: https://proceedings.neurips.cc/paper/2018/hash/dc5d637ed5e62c36ecb73b654b05ba2a-Abstract.html.
  260. Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, and Luc De Raedt. Neural probabilistic logic programming in DeepProbLog. Artificial Intelligence, 298:103504, 2021. URL: https://doi.org/10.1016/j.artint.2021.103504.
  261. V. Marek and M. Truszczynski. Stable Semantics for Logic Programs and Default Theories. In Ewing L. Lusk and Ross A. Overbeek, editors, Proceedings of the North American Conference on Logic Programming, NACLP 1989, pages 243-256. MIT Press, 1989. Google Scholar
  262. Victor W. Marek and Miroslaw Truszczynski. Nonmonotonic Logic: Context-dependent Reasoning. Springer, 1993. Google Scholar
  263. Victor W. Marek and Miroslaw Truszczynski. Stable models and an alternative logic programming paradigm. In Krzysztof R. Apt, Victor W. Marek, Mirek Truszczynski, and David Scott Warren, editors, The Logic Programming Paradigm - A 25-Year Perspective, Artificial Intelligence, pages 375-398. Springer, 1999. URL: https://doi.org/10.1007/978-3-642-60085-2_17.
  264. Giuseppe Marra and Ondrej Kuzelka. Neural markov logic networks. In Cassio P. de Campos, Marloes H. Maathuis, and Erik Quaeghebeur, editors, Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, UAI 2021, Virtual Event, volume 161 of Proceedings of Machine Learning Research, pages 908-917. AUAI Press, 2021. URL: https://proceedings.mlr.press/v161/marra21a.html.
  265. J. McCarthy. Epistemological problems in artificial intelligence. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 1038-1044, Cambridge, MA, 1977. URL: http://ijcai.org/Proceedings/77-2/Papers/094.pdf.
  266. J. McCarthy. First order theories of individual concepts and propositions. In D. Michie, editor, Machine Intelligence 9, pages 129-147. Edinburgh University Press, 1979. Google Scholar
  267. J. McCarthy. Circumscription - a form of non-monotonic reasoning. Artificial Intelligence Journal, 13:27-39, 1980. URL: https://doi.org/10.1016/0004-3702(80)90011-9.
  268. J. McCarthy and P.J. Hayes. Some philosophical problems from the standpoint of artificial intelligence. In D. Michie and B. Meltzer, editors, Machine Intelligence 4, pages 463-502. Edinburgh University Press, 1969. Google Scholar
  269. John McCarthy. Programs with Common Sense, 1959. URL: http://jmc.stanford.edu/articles/mcc59/mcc59.pdf.
  270. John McCarthy. Situations, actions, and causal laws. Technical report, Stanford University, Department of Computer Science, 1963. Google Scholar
  271. John McCarthy and Patrick J. Hayes. Some philosophical problems from the standpoint of artificial intelligence. In B. Meltzer and D. Michie, editors, Machine Intelligence 4, pages 463-502. Edinburgh University Press, 1969. reprinted in McC90. Google Scholar
  272. Drew McDermott. Nonmonotonic Logic II: Nonmonotonic Modal Theories. Journal of the ACM (JACM), 29(1):33-57, 1982. URL: https://doi.org/10.1145/322290.322293.
  273. Drew McDermott and Jon Doyle. Non-monotonic Logic I. Artificial intelligence, 13(1-2):41-72, 1980. URL: https://doi.org/10.1016/0004-3702(80)90012-0.
  274. John P. McDermott. R1: an Expert in the Computer Systems Domain. In Robert Balzer, editor, Proceedings of the 1st Annual National Conference on Artificial Intelligence, AAAI 1980, pages 269-271. AAAI Press/MIT Press, 1980. URL: http://www.aaai.org/Library/AAAI/1980/aaai80-076.php.
  275. Deborah L. McGuinness and Frank Van Harmelen. OWL Web Ontology Language Overview. W3C recommendation, 10(10):2004, 2004. Google Scholar
  276. Thomas Andreas Meyer, Kevin Lee, and Richard Booth. Knowledge integration for description logics. In Manuela M. Veloso and Subbarao Kambhampati, editors, Proceedings, The Twentieth National Conference on Artificial Intelligence, pages 645-650. AAAI Press / The MIT Press, 2005. URL: http://www.aaai.org/Library/AAAI/2005/aaai05-101.php.
  277. Marvin Minsky. A Framework for Representing Knowledge. MIT-AI Laboratory Memo 306, June, 1974, Reprinted in The Psychology of Computer Vision, P. Winston (Ed.), McGraw-Hill, 1975. Google Scholar
  278. Raquel Mochales and Marie-Francine Moens. Argumentation mining. Artificial Intelligence and Law, 19:1-22, 2011. URL: https://doi.org/10.1007/s10506-010-9104-x.
  279. Sanjay Modgil and Henry Prakken. A general account of argumentation with preferences. Artificial Intelligence, 195:361-397, 2013. URL: https://doi.org/10.1016/j.artint.2012.10.008.
  280. Sanjay Modgil and Henry Prakken. Abstract rule-based argumentation. In Pietro Baroni, Dov Gabbay, Massimilino Giacomin, and Leendert Van der Torre, editors, Handbook of Formal Argumentation, pages 287-364. College Publications, 2018. Google Scholar
  281. Kodylan Moodley, Thomas Meyer, and Uli Sattler. DIP: A defeasible-inference platform for OWL ontologies. In Meghyn Bienvenu, Magdalena Ortiz, Riccardo Rosati, and Mantas Simkus, editors, Informal Proceedings of the 27th International Workshop on Description Logics, volume 1193 of CEUR Workshop Proceedings, pages 671-683. CEUR-WS.org, 2014. URL: http://ceur-ws.org/Vol-1193/paper_44.pdf.
  282. Robert C. Moore. Possible-world semantics for autoepistemic logic. In Proceedings of the Non-Monotonic Reasoning Workshop, pages 344-354. American Association for Artificial Intelligence (AAAI), 1984. Google Scholar
  283. Robert C Moore. Semantical Considerations on Nonmonotonic Logic. Artificial Intelligence, 25(1):75-94, 1985. URL: https://doi.org/10.1016/0004-3702(85)90042-6.
  284. Boris Motik, Bernardo Cuenca Grau, Ian Horrocks, Zhe Wu, Achille Fokoue, and Carsten Lutz, editors. OWL 2 Web Ontology Language: Profiles. W3C Recommendation, 27 October 2009. URL: http://www.w3.org/TR/owl2-profiles/.
  285. Stephen Muggleton. Inductive logic programming. New Generation Computing, 8(4):295-318, 1991. URL: https://doi.org/10.1007/BF03037089.
  286. Stephen H. Muggleton and Luc De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19/20:629-679, 1994. URL: https://doi.org/10.1016/0743-1066(94)90035-3.
  287. Nina Narodytska. Formal verification of deep neural networks. In Nikolaj S. Bjørner and Arie Gurfinkel, editors, 2018 Formal Methods in Computer Aided Design, FMCAD 2018, page 1. IEEE, 2018. URL: https://doi.org/10.23919/FMCAD.2018.8603017.
  288. Nina Narodytska, Shiva Prasad Kasiviswanathan, Leonid Ryzhyk, Mooly Sagiv, and Toby Walsh. Verifying properties of binarized deep neural networks. In Sheila A. McIlraith and Kilian Q. Weinberger, editors, AAAI, pages 6615-6624. AAAI Press, 2018. URL: https://doi.org/10.1609/AAAI.V32I1.12206.
  289. A.C. Nayak, M. Pagnucco, and P. Peppas. Dynamic belief revision operators. Artificial Intelligence, 146(2):193-228, 2003. URL: https://doi.org/10.1016/S0004-3702(03)00017-1.
  290. Mojtaba Nayyeri, Chengjin Xu, Mirza Mohtashim Alam, Jens Lehmann, and Hamed Shariat Yazdi. Logicenn: A neural based knowledge graphs embedding model with logical rules. IEEE Transactions on Pattern Analysis of Machince Intelligence, 45(6):7050-7062, 2023. URL: https://doi.org/10.1109/TPAMI.2021.3121646.
  291. Yavor Nenov, Robert Piro, Boris Motik, Ian Horrocks, Zhe Wu, and Jay Banerjee. Rdfox: A highly-scalable RDF store. In Marcelo Arenas, Óscar Corcho, Elena Simperl, Markus Strohmaier, Mathieu d'Aquin, Kavitha Srinivas, Paul Groth, Michel Dumontier, Jeff Heflin, Krishnaprasad Thirunarayan, and Steffen Staab, editors, The Semantic Web - ISWC 2015 - 14th International Semantic Web Conference, volume 9367 of Lecture Notes in Computer Science, pages 3-20. Springer, 2015. URL: https://doi.org/10.1007/978-3-319-25010-6_1.
  292. Allen Newell, J. C. Shaw, and Herbert A. Simon. Empirical Explorations of the Logic Theory Machine: a Case Study in Heuristic. In Morton M. Astrahan, editor, Western Joint Computer Conference: Techniques for Reliability, IRE-AIEE-ACM 1957 (Western), pages 218-230. ACM, 1957. URL: https://doi.org/10.1145/1455567.1455605.
  293. Allen Newell and Herbert A. Simon. The Logic Theory Machine-A Complex Information Processing System. IRE Trans. Inf. Theory, 2(3):61-79, 1956. URL: https://doi.org/10.1109/TIT.1956.1056797.
  294. Ilkka Niemelä. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence, 25(3-4):241-273, 1999. URL: https://doi.org/10.1023/A:1018930122475.
  295. Ilkka Niemelä and Patrik Simons. Smodels - An Implementation of the Stable Model and Well-Founded Semantics for Normal LP. In Proceedings of the 4th International Conference on Logic Programming and Nonmotonic Reasoning, LPNMR 1997, volume 1265 of Lecture Notes in Computer Science, pages 421-430. Springer, 1997. URL: https://doi.org/10.1007/3-540-63255-7_32.
  296. Nils J. Nilsson. Shakey the robot, 1984. SRI International, Menlo Park, California. Google Scholar
  297. Nils J. Nilsson. Probabilistic logic. Artificial Intelligence, 28(1):71-87, 1986. URL: https://doi.org/10.1016/0004-3702(86)90031-7.
  298. Ahmed Nouman, Volkan Patoglu, and Esra Erdem. Hybrid conditional planning for robotic applications. Int. J. Robotics Res., 40(2-3), 2021. URL: https://doi.org/10.1177/0278364920963783.
  299. Z. Ognjanovic and M. Raškovic. Some first-order probability logics. Theoretical Computer Science, 247(1-2):191-212, 2000. URL: https://doi.org/10.1016/S0304-3975(98)00341-7.
  300. Jukka Pajunen and Tomi Janhunen. Solution enumeration by optimality in answer set programming. CoRR, abs/2108.03474, 2021. URL: https://doi.org/10.48550/arXiv.2108.03474.
  301. Paolo Pareti and George Konstantinidis. A review of SHACL: from data validation to schema reasoning for RDF graphs. In Reasoning Web. Declarative Artificial Intelligence - 17th International Summer School 2021, pages 115-144, 2021. URL: https://doi.org/10.1007/978-3-030-95481-9_6.
  302. Judea Pearl. Bayesian networks: A model of self-activated memory for evidential reasoning. In Proceedings of the 7th Conference of the Cognitive Science Society, pages 15-17, 1985. Google Scholar
  303. Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988. Google Scholar
  304. Judea Pearl. Graphical models for probabilistic and causal reasoning. In Quantified Representation of Uncertainty and Imprecision, pages 367-389. Springer, 1998. Google Scholar
  305. Rafael Peñaloza. Axiom pinpointing. In Giuseppe Cota, Marilena Daquino, and Gian Luca Pozzato, editors, Applications and Practices in Ontology Design, Extraction, and Reasoning, volume 49 of Studies on the Semantic Web, pages 162-177. IOS Press, 2020. URL: https://doi.org/10.3233/SSW200042.
  306. Maximilian Pensel and Anni-Yasmin Turhan. Reasoning in the defeasible description logic εl - computing standard inferences under rational and relevant semantics. International Journal of Approximate Reasoning, 103:28-70, 2018. URL: https://doi.org/10.1016/J.IJAR.2018.08.005.
  307. P. Peppas. Belief revision. In F. van Harmelen, V. Lifschitz, and B. Porter, editors, Handbook of Knowledge Representation, pages 317-359. Elsevier Science, San Diego, USA, 2008. URL: https://doi.org/10.1016/S1574-6526(07)03008-8.
  308. Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick S. H. Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander H. Miller. Language models as knowledge bases? In Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan, editors, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP, pages 2463-2473. Association for Computational Linguistics, 2019. URL: https://doi.org/10.18653/v1/D19-1250.
  309. Giulio Petrucci, Marco Rospocher, and Chiara Ghidini. Expressive ontology learning as neural machine translation. Journal of Web Semantics, 52-53:66-82, 2018. URL: https://doi.org/10.1016/j.websem.2018.10.002.
  310. Potassco project, 2011+. https://potassco.org. URL: https://github.com/potassco/.
  311. Henry Prakken and Rosa Ratsma. A top-level model of case-based argumentation for explanation: Formalisation and experiments. Argument & Computation, 13(2):159-194, 2022. URL: https://doi.org/10.3233/AAC-210009.
  312. Marcelo O. R. Prates, Pedro H. C. Avelar, Henrique Lemos, Luís C. Lamb, and Moshe Y. Vardi. Learning to solve np-complete problems: A graph neural network for decision TSP. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI, pages 4731-4738. AAAI Press, 2019. URL: https://doi.org/10.1609/aaai.v33i01.33014731.
  313. Anna Queralt, Alessandro Artale, Diego Calvanese, and Ernest Teniente. Ocl-lite: Finite reasoning on UML/OCL conceptual schemas. Data Knowledge Engineering, 73:1-22, 2012. URL: https://doi.org/10.1016/j.datak.2011.09.004.
  314. M Ross Quillian. Word Concepts: A Theory and Simulation of Some Basic Semantic Capabilities. Behavioral Science, 12(5):410-430, 1967. Google Scholar
  315. L. De Raedt and K. Kersting. Probabilistic inductive logic programming. In Probabilistic Inductive Logic Programming, pages 1-27, 2008. URL: https://doi.org/10.1007/978-3-540-78652-8_1.
  316. L. De Raedt, A. Kimmig, and H. Toivonen. Problog: A probabilistic prolog and its application in link discovery. In Proc. IJCAI, pages 2462-2467, 2007. URL: http://ijcai.org/Proceedings/07/Papers/396.pdf.
  317. Marco Ragni, Gabriele Kern-Isberner, Christoph Beierle, and Kai Sauerwald. Cognitive logics - features, formalisms, and challenges. In Giuseppe De Giacomo and Jérôme Lang, editors, ECAI 2020 - 24th European Conference on Artificial Intelligence, volume 325, pages 2931-2932. IOS Press, 2020. URL: https://doi.org/10.3233/FAIA200459.
  318. Antonio Rago, Oana Cocarascu, Christos Bechlivanidis, and Francesca Toni. Argumentation as a Framework for Interactive Explanations for Recommendations. In Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning, pages 805-815, sep 2020. URL: https://doi.org/10.24963/kr.2020/83.
  319. Manfred Reichert and Barbara Weber. Enabling Flexibility in Process-Aware Information Systems - Challenges, Methods, Technologies. Springer, 2012. URL: https://doi.org/10.1007/978-3-642-30409-5.
  320. R. Reiter. On closed world data bases. In H. Gallaire and J. Minker, editors, Logic and Databases, pages 55-76. Plenum Press, 1978. URL: https://doi.org/10.1007/978-1-4684-3384-5_3.
  321. R. Reiter. A logic for default reasoning. Artificial Intelligence Journal, 13(1-2):81-132, 1980. URL: https://doi.org/10.1016/0004-3702(80)90014-4.
  322. R. Reiter. Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems. The MIT Press, Cambridge, MA, 2001. Google Scholar
  323. Raymond Reiter. On Closed World Data Bases. In Herve Gallaire and Jack Minker, editors, Logic and Data Bases, pages 55-76. Plenum Press, 1978. URL: https://doi.org/10.1007/978-1-4684-3384-5_3.
  324. Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, and Rajeev Rastogi, editors, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1135-1144. ACM, 2016. URL: https://doi.org/10.1145/2939672.2939778.
  325. Momina Rizwan, Volkan Patoglu, and Esra Erdem. Human robot collaborative assembly planning: An answer set programming approach. Theory and Practice of Logic Programming, 20(6):1006-1020, 2020. URL: https://doi.org/10.1017/S1471068420000319.
  326. Mark Roberts, Laura M. Hiatt, Alexandra Coman, Dongkyu Choi, Benjamin Johnson, and David W. Aha. Actorsim, A toolkit for studying cross-disciplinary challenges in autonomy. In 2016 AAAI Fall Symposia, Arlington, Virginia, USA, November 17-19, 2016. AAAI Press, 2016. URL: https://aaai.org/papers/14130-14130-actorsim-a-toolkit-for-studying-cross-disciplinary-challenges-in-autonomy/.
  327. John Alan Robinson. A machine-oriented logic based on the resolution principle. Journal of the ACM, 12(1):23-41, 1965. URL: https://doi.org/10.1145/321250.321253.
  328. Stuart J. Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Pearson Education, third edition, 2010. Google Scholar
  329. S. Sanghai, P. Domingos, and D. Weld. Relational Dynamic Bayesian Networks. Journal of Artificial Intelligence Research, 24:759-797, 2005. URL: https://doi.org/10.1613/jair.1625.
  330. Abulhair Saparov and He He. Language models are greedy reasoners: A systematic formal analysis of chain-of-thought. CoRR, abs/2210.01240, 2022. URL: https://doi.org/10.48550/arXiv.2210.01240.
  331. Roger C. Schank and Robert P. Abelson. Scripts, Plans and Knowledge. In The 4th International Joint Conference on Artificial Intelligence, IJCAI 1975, pages 151-157, 1975. URL: http://ijcai.org/Proceedings/75/Papers/021.pdf.
  332. Klaus Schild. A correspondence theory for terminological logics: Preliminary report. In John Mylopoulos and Raymond Reiter, editors, Proceedings of the 12th International Joint Conference on Artificial Intelligence, pages 466-471, Sydney, Australia, 1991. URL: http://ijcai.org/Proceedings/91-1/Papers/072.pdf.
  333. Jodi Schneider, Tudor Groza, and Alexandre Passant. A review of argumentation for the social semantic web. Semantic Web, 4(2):159-218, 2013. URL: https://doi.org/10.3233/SW-2012-0073.
  334. Grigori Schwarz and Miroslaw Truszczynski. Minimal knowledge problem: A new approach. Artificial Intelligence, 67(1):113-141, 1994. URL: https://doi.org/10.1016/0004-3702(94)90013-2.
  335. Steven Shapiro, Maurice Pagnucco, Yves Lespérance, and Hector J. Levesque. Iterated belief change in the situation calculus. Artificial Intelligence, 175(1):165-192, 2011. URL: https://doi.org/10.1016/j.artint.2010.04.003.
  336. Yoav Shoham. A semantical approach to nonmonotonic logics. In Proceedings of Logics in Computer Science, pages 275-279, Ithaca, New York, 1987. Google Scholar
  337. Edward Shortliffe. Computer-based Medical Consultations: MYCIN. Addison-Wesley, 1976. Google Scholar
  338. Evren Sirin, Bijan Parsia, Bernardo Cuenca Grau, Aditya Kalyanpur, and Yarden Katz. Pellet: A practical OWL-DL reasoner. Journal of Web Semantics, 5(2):51-53, 2007. URL: https://doi.org/10.1016/j.websem.2007.03.004.
  339. B.C. Smith. Reflections and Semantics in a Procedural Language. PhD thesis, MIT, 1982. Google Scholar
  340. Lars-Phillip Spiegel, Gabriele Kern-Isberner, and Marco Ragni. Rational inference patterns. In Abhaya C. Nayak and Alok Sharma, editors, PRICAI 2019: 16th Pacific Rim International Conference on Artificial Intelligence, volume 11670 of Lecture Notes in Computer Science, pages 405-417. Springer, 2019. URL: https://doi.org/10.1007/978-3-030-29908-8_33.
  341. W. Spohn. Ordinal conditional functions: A dynamic theory of epistemic states. In W.L. Harper and B. Skyrms, editors, Causation in Decision, Belief Change, and Statistics, volume II, pages 105-134. Kluwer Academic Publishers, 1988. Google Scholar
  342. Andreas Steigmiller, Thorsten Liebig, and Birte Glimm. Konclude: System description. Journal of Web Semantics, 27:78-85, 2014. URL: https://doi.org/10.1016/j.websem.2014.06.003.
  343. Erik Strumbelj and Igor Kononenko. Explaining prediction models and individual predictions with feature contributions. Knowledge Information Systems, 41(3):647-665, 2014. URL: https://doi.org/10.1007/s10115-013-0679-x.
  344. Heiner Stuckenschmidt, Christine Parent, and Stefano Spaccapietra, editors. Modular Ontologies: Concepts, Theories and Techniques for Knowledge Modularization, volume 5445 of Lecture Notes in Computer Science. Springer, 2009. URL: https://doi.org/10.1007/978-3-642-01907-4.
  345. Rudi Studer, V.Richard Benjamins, and Dieter Fensel. Knowledge engineering: Principles and methods. Data & Knowledge Engineering, 25(1):161-197, 1998. URL: https://doi.org/10.1016/S0169-023X(97)00056-6.
  346. D. Suciu, D. Olteanu, C. Ré, and C. Koch. Probabilistic databases, volume 3 of Synthesis Lectures on Data Management. Morgan & Claypool Publishers, 2011. URL: https://doi.org/10.2200/S00362ED1V01Y201105DTM016.
  347. Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. Rotate: Knowledge graph embedding by relational rotation in complex space. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net, 2019. URL: https://openreview.net/forum?id=HkgEQnRqYQ.
  348. Tommi Syrjänen and Ilkka Niemelä. The smodels system. In Proceedings of the 6th International Conference on Logic Programming and Nonmotonic Reasoning, LPNMR 2001, volume 2173 of Lecture Notes in Computer Science, pages 434-438. Springer, 2001. URL: https://doi.org/10.1007/3-540-45402-0_38.
  349. Moritz Tenorth and Michael Beetz. Representations for robot knowledge in the knowrob framework. Artificial Intelligence, 247:151-169, 2017. URL: https://doi.org/10.1016/j.artint.2015.05.010.
  350. Simone Teufel, Advaith Siddharthan, and Colin Batchelor. Towards domain-independent argumentative zoning: Evidence from chemistry and computational linguistics. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 1493-1502, 2009. URL: https://aclanthology.org/D09-1155/.
  351. Michael Thielscher. Ramification and causality. Artificial Intelligence Journal, 89(1-2):317-364, 1997. URL: https://doi.org/10.1016/S0004-3702(96)00033-1.
  352. Sebastian Thrun, Michael Beetz, Maren Bennewitz, Wolfram Burgard, Armin B. Cremers, Frank Dellaert, Dieter Fox, Dirk Hähnel, Charles R. Rosenberg, Nicholas Roy, Jamieson Schulte, and Dirk Schulz. Probabilistic algorithms and the interactive museum tour-guide robot minerva. International Journal of Robotics Research, 19(11):972-999, 2000. URL: https://doi.org/10.1177/02783640022067922.
  353. Sebastian Thrun, Wolfram Burgard, and Dieter Fox. Probabilistic robotics. Intelligent robotics and autonomous agents. MIT Press, 2005. Google Scholar
  354. Ilaria Tiddi and Stefan Schlobach. Knowledge graphs as tools for explainable machine learning: A survey. Artif. Intell., 302:103627, 2022. URL: https://doi.org/10.1016/J.ARTINT.2021.103627.
  355. David S. Touretzky. The Mathematics of Inheritance Systems. Morgan Kaufmann, 1986. Google Scholar
  356. Théo Trouillon, Christopher R. Dance, Éric Gaussier, Johannes Welbl, Sebastian Riedel, and Guillaume Bouchard. Knowledge graph completion via complex tensor factorization. Journal of Machine Learning Research, 18:130:1-130:38, 2017. URL: http://jmlr.org/papers/v18/16-563.html.
  357. Johan van Benthem. Epistemic logic and epistemology: The state of their affairs. Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition, 128(1):49-76, 2006. URL: http://www.jstor.org/stable/4321713.
  358. Wil M. P. van der Aalst. Process Mining - Data Science in Action, Second Edition. Springer, 2016. URL: https://doi.org/10.1007/978-3-662-49851-4.
  359. Frank van Harmelen, Vladimir Lifschitz, and Bruce W. Porter, editors. Handbook of Knowledge Representation, volume 3 of Foundations of Artificial Intelligence. Elsevier, 2008. URL: https://www.sciencedirect.com/science/bookseries/15746526/3.
  360. Jean van Heijenoort. From Frege to Gödel: A Source Book in Mathematical Logic, 1879-1931. Cambridge, MA, USA: Harvard University Press, 1967. Google Scholar
  361. Linde Vanbesien, Maurice Bruynooghe, and Marc Denecker. Analyzing semantics of aggregate answer set programming using approximation fixpoint theory. Theory and Practice of Logic Programming, 22(4):523-537, 2022. URL: https://doi.org/10.1017/S1471068422000126.
  362. Serena Villata and Elena Cabrio. Five years of argument mining: a data-driven analysis. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018. International Joint Conferences on Artificial Intelligence Organization, 2018. URL: https://doi.org/10.24963/ijcai.2018/766.
  363. Denny Vrandečić and Markus Krötzsch. Wikidata: A free collaborative knowledgebase. Communications of the ACM, 57(10):78-85, sep 2014. URL: https://doi.org/10.1145/2629489.
  364. W3C OWL Working Group. OWL 2 web ontology language document overview. URL, 2009. URL: http://www.w3.org/TR/owl2-overview/.
  365. Przemyslaw Andrzej Walega, Michal Zawidzki, and Bernardo Cuenca Grau. Finite materialisability of datalog programs with metric temporal operators. Journal of Artificial Intelligence Research, 76, 2023. URL: https://doi.org/10.1613/jair.1.14040.
  366. Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering, 29:2724-2743, 2017. URL: https://doi.org/10.1109/TKDE.2017.2754499.
  367. Wasp project, 2013+. URL: http://alviano.github.io/wasp/.
  368. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed H. Chi, Quoc Le, and Denny Zhou. Chain of thought prompting elicits reasoning in large language models. CoRR, abs/2201.11903, 2022. https://arxiv.org/abs/2201.11903, URL: https://doi.org/10.48550/arXiv.2201.11903.
  369. Antonius Weinzierl, Richard Taupe, and Gerhard Friedrich. Advancing lazy-grounding ASP solving techniques - restarts, phase saving, heuristics, and more. Theory and Practice of Logic Programming, 20(5):609-624, 2020. URL: https://doi.org/10.1017/S1471068420000332.
  370. Alfred North Whitehead and Bertrand Arthur William Russell. Principia Mathematica; 2nd ed. Cambridge University Press, Cambridge, 1927. URL: https://cds.cern.ch/record/268025.
  371. Sarah Wiegreffe and Ana Marasovic. Teach me to explain: A review of datasets for explainable natural language processing. In Joaquin Vanschoren and Sai-Kit Yeung, editors, Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, virtual, 2021. URL: https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/698d51a19d8a121ce581499d7b701668-Abstract-round1.html.
  372. Hong Wu, Zhe Wang, Kewen Wang, and Yi-Dong Shen. Learning Typed Rules over Knowledge Graphs. In Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning, pages 494-503, aug 2022. URL: https://doi.org/10.24963/kr.2022/51.
  373. Guohui Xiao, Diego Calvanese, Roman Kontchakov, Domenico Lembo, Antonella Poggi, Riccardo Rosati, and Michael Zakharyaschev. Ontology-based data access: A survey. In Jérôme Lang, editor, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, pages 5511-5519. ijcai.org, 2018. URL: https://doi.org/10.24963/ijcai.2018/777.
  374. Guohui Xiao, Davide Lanti, Roman Kontchakov, Sarah Komla-Ebri, Elem Güzel Kalayci, Linfang Ding, Julien Corman, Benjamin Cogrel, Diego Calvanese, and Elena Botoeva. The virtual knowledge graph system ontop. In Jeff Z. Pan, Valentina A. M. Tamma, Claudia d'Amato, Krzysztof Janowicz, Bo Fu, Axel Polleres, Oshani Seneviratne, and Lalana Kagal, editors, The Semantic Web - ISWC 2020 - 19th International Semantic Web Conference, volume 12507 of Lecture Notes in Computer Science, pages 259-277. Springer, 2020. URL: https://doi.org/10.1007/978-3-030-62466-8_17.
  375. Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, and Guy Van den Broeck. A semantic loss function for deep learning with symbolic knowledge. In Jennifer G. Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, volume 80 of Proceedings of Machine Learning Research, pages 5498-5507. PMLR, 2018. URL: http://proceedings.mlr.press/v80/xu18h.html.
  376. Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. Embedding entities and relations for learning and inference in knowledge bases. In Yoshua Bengio and Yann LeCun, editors, 3rd International Conference on Learning Representations, ICLR 2015, 2015. URL: https://doi.org/10.48550/arXiv.1412.6575.
  377. Zhun Yang, Adam Ishay, and Joohyung Lee. NeurASP: Embracing neural networks into answer set programming. In Christian Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI, pages 1755-1762, 2020. URL: https://doi.org/10.24963/ijcai.2020/243.
  378. Safdar Zaman, Gerald Steinbauer, Johannes Maurer, Peter Lepej, and Suzana Uran. An integrated model-based diagnosis and repair architecture for ros-based robot systems. In 2013 IEEE International Conference on Robotics and Automation, pages 482-489, 2013. URL: https://doi.org/10.1109/ICRA.2013.6630618.
  379. Benjamin Zarrieß. Complexity of projection with stochastic actions in a probabilistic description logic. In Michael Thielscher, Francesca Toni, and Frank Wolter, editors, Principles of Knowledge Representation and Reasoning: Proceedings of the Sixteenth International Conference, KR 2018, pages 514-523. AAAI Press, 2018. URL: https://aaai.org/ocs/index.php/KR/KR18/paper/view/18050.
  380. Rowan Zellers, Jiasen Lu, Ximing Lu, Youngjae Yu, Yanpeng Zhao, Mohammadreza Salehi, Aditya Kusupati, Jack Hessel, Ali Farhadi, and Yejin Choi. MERLOT RESERVE: neural script knowledge through vision and language and sound. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, pages 16354-16366. IEEE, 2022. URL: https://doi.org/10.1109/CVPR52688.2022.01589.
  381. Maximilian Zellner, Ali E Abbas, David V Budescu, and Aram Galstyan. A survey of human judgement and quantitative forecasting methods. Royal Society Open Science, 8(2):201187, 2021. Google Scholar
  382. Honghua Zhang, Liunian Harold Li, Tao Meng, Kai-Wei Chang, and Guy Van den Broeck. On the paradox of learning to reason from data. CoRR, abs/2205.11502, 2022. URL: https://doi.org/10.48550/arXiv.2205.11502.
  383. Zheng Zhang, Levent Yilmaz, and Bo Liu. A critical review of inductive logic programming techniques for explainable AI. CoRR, abs/2112.15319, 2021. URL: https://doi.org/10.48550/arXiv.2112.15319.
  384. Yizheng Zhao and Renate A. Schmidt. FAME: an automated tool for semantic forgetting in expressive description logics. In Didier Galmiche, Stephan Schulz, and Roberto Sebastiani, editors, Automated Reasoning - 9th International Joint Conference, IJCAR, volume 10900 of Lecture Notes in Computer Science, pages 19-27. Springer, 2018. URL: https://doi.org/10.1007/978-3-319-94205-6_2.
  385. Zhiqiang Zhuang, Zhe Wang, Kewen Wang, and Guilin Qi. DL-Lite contraction and revision. Journal of Artificial Intelligence Research, 56:328-378, 2016. URL: https://doi.org/10.1613/jair.5050.
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