Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
GPT-5.1
GPT-5.1 114 tok/s
Gemini 3.0 Pro 53 tok/s Pro
Gemini 2.5 Flash 132 tok/s Pro
Kimi K2 176 tok/s Pro
Claude Sonnet 4.5 37 tok/s Pro
2000 character limit reached

Model Explanation via Support Graphs (2310.01626v1)

Published 2 Oct 2023 in cs.LO

Abstract: In this note, we introduce the notion of support graph to define explanations for any model of a logic program. An explanation is an acyclic support graph that, for each true atom in the model, induces a proof in terms of program rules represented by labels. A classical model may have zero, one or several explanations: when it has at least one, it is called a justified model. We prove that all stable models are justified whereas, in general, the opposite does not hold, at least for disjunctive programs. We also provide a meta-programming encoding in Answer Set Programming that generates the explanations for a given stable model of some program. We prove that the encoding is sound and complete, that is, there is a one-to-one correspondence between each answer set of the encoding and each explanation for the original stable model.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (23)
  1. Forgetting auxiliary atoms in forks, 2017. unpublished draft.
  2. Justifications for goal-directed constraint answer set programming. In Intl. Conf. on Logic Programming, ICLP, 2020.
  3. An abstract, argumentation-theoretic approach to default reasoning. Artificial Intelligence, 93(1):63–101, 1997.
  4. Answer set programming at a glance. Communications of the ACM, 54(12):92–103, 2011.
  5. Explanation graphs for stable models of labelled logic programs. In Joaquín Arias, Sotiris Batsakis, Wolfgang Faber, Gopal Gupta, Francesco Pacenza, Emmanuel Papadakis, Livio Robaldo, Kilian Rückschloß, Elmer Salazar, Zeynep Gozen Saribatur, Ilias Tachmazidis, Felix Weitkämper, and Adam Z. Wyner, editors, Proceedings of the International Conference on Logic Programming 2023 Workshops co-located with the 39th International Conference on Logic Programming (ICLP 2023), London, United Kingdom, July 9th and 10th, 2023, volume 3437 of CEUR Workshop Proceedings. CEUR-WS.org, 2023. URL https://ceur-ws.org/Vol-3437/paper3ASPOCP.pdf.
  6. Causal graph justifications of logic programs. Theory and Practice of Logic Programming, 14:603–618, 09 2014.
  7. K. L. Clark. Negation as failure. In H. Gallaire and J. Minker, editors, Logic and Databases, pages 293–322. Plenum, 1978.
  8. A formal theory of justifications. In Francesco Calimeri, Giovambattista Ianni, and Miroslaw Truszczynski, editors, Logic Programming and Nonmonotonic Reasoning - 13th Intl. Conf., LPNMR 2015, Lexington, KY, USA, 2015. Proceedings, volume 9345 of Lecture Notes in Computer Science. Springer, 2015.
  9. Assumption-based argumentation. Argumentation in Artificial Intelligence, pages 199–218, 05 2009.
  10. Abstraction for zooming-in to unsolvability reasons of grid-cell problems. In Intl. Joint Conf. on Artificial Intelligence IJCAI 2019, Workshop on Explainable Artificial Intelligence, 09 2019.
  11. Generating explanations for complex biomedical queries. Theory and Practice of Logic Programming, 15, 09 2013.
  12. Jorge Fandinno. A Causal Semantics for Logic Programming. PhD thesis, Facultad de Informática, University of A Coruña, 2015.
  13. Answering the ”why” in answer set programming - A survey of explanation approaches. Theory and Practice of Logic Programming, 19(2):114–203, 2019.
  14. A meta-programming technique for debugging answer-set programs. In Dieter Fox and Carla P. Gomes, editors, Proc. of the 23rd AAAI Conf. on Artificial Intelligence, Chicago, IL, USA. AAAI Press, 2008.
  15. The stable models semantics for logic programming. In Proc. of the 5th Intl. Conf. on Logic Programming, pages 1070–1080, 1988.
  16. Simon Marynissen. Advances in Justification Theory. PhD thesis, Department of Computer Science, KU Leuven, 2022. Denecker, Marc and Bart Bogaerts (supervisors).
  17. Judea Pearl. Reasoning with cause and effect. In Thomas Dean, editor, Proc. of the 16th Intl. Joint Conf. on Artificial Intelligence, IJCAI 99, Stockholm, Sweden. Morgan Kaufmann, 1999.
  18. Justifications for logic programs under answer set semantics. In Sandro Etalle and Mirosław Truszczyński, editors, Logic Programming, pages 196–210, Berlin, Heidelberg, 2006. Springer Berlin Heidelberg.
  19. Abstraction for non-ground answer set programs. Artificial Intelligence, 300:103563, 2021.
  20. Justifying answer sets using argumentation. Theory and Practice of Logic Programming, 16(1):59–110, 2016.
  21. Determining inference semantics for disjunctive logic programs. Artificial Intelligence, 277, 2019.
  22. xasp: An explanation generation system for answer set programming. In Georg Gottlob, Daniela Inclezan, and Marco Maratea, editors, Logic Programming and Nonmonotonic Reasoning - 16th International Conference, LPNMR 2022, Genova, Italy, September 5-9, 2022, Proceedings, volume 13416 of Lecture Notes in Computer Science, pages 363–369. Springer, 2022.
  23. M. H. van Emden and R. A. Kowalski. The semantics of predicate logic as a programming language. Journal of the ACM, 23:733–742, 1976.
Citations (1)

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.