Document Open Access Logo

Simplifying Step-Wise Explanation Sequences

Authors Ignace Bleukx , Jo Devriendt , Emilio Gamba , Bart Bogaerts , Tias Guns

PDF
Thumbnail PDF

File

LIPIcs.CP.2023.11.pdf
  • Filesize: 1 MB
  • 20 pages

Document Identifiers

Author Details

Ignace Bleukx
  • DTAI, KU Leuven, Belgium
Jo Devriendt
  • DTAI, KU Leuven, Belgium
Emilio Gamba
  • Data Analytics Lab, VUB, Brussels, Belgium
  • DTAI, KU Leuven, Belgium
Bart Bogaerts
  • Artificial Intelligence Lab, VUB, Brussels, Belgium
Tias Guns
  • DTAI, KU Leuven, Belgium
  • Data Analytics Lab, VUB, Brussels, Belgium

Funding

This research was partly funded by the Flemish Government (AI Research Program), the Research Foundation - Flanders (FWO) project G070521N; the European Research Council (ERC) under the EU Horizon 2020 research and innovation programme (Grant No 101002802, CHAT-Opt) and the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101070149, project Tuples.

Cite As Get BibTex

Ignace Bleukx, Jo Devriendt, Emilio Gamba, Bart Bogaerts, and Tias Guns. Simplifying Step-Wise Explanation Sequences. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.CP.2023.11

Abstract

Explaining constraint programs is useful for debugging an unsatisfiable program, to understand why a given solution is optimal, or to understand how to find a unique solution. A recently proposed framework for explaining constraint programs works well to explain the unique solution to a problem step by step. It can also be used to step-wise explain why a model is unsatisfiable, but this may create redundant steps and introduce superfluous information into the explanation sequence. This paper proposes methods to simplify a (step-wise) explanation sequence, to generate simple steps that together form a short, interpretable sequence. We propose an algorithm to greedily construct an initial sequence and two filtering algorithms that eliminate redundant steps and unnecessarily complex parts of explanation sequences. Experiments on diverse benchmark instances show that our techniques can significantly simplify step-wise explanation sequences.

Subject Classification

ACM Subject Classification
  • Theory of computation → Constraint and logic programming
Keywords
  • explanation
  • deduction
  • constraint programming
  • propagation

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Supplementary Materials

References

  1. Abderrahmane Aggoun and Nicolas Beldiceanu. Extending CHIP in order to solve complex scheduling and placement problems. In Jean-Paul Delahaye, Philippe Devienne, Philippe Mathieu, and Pascal Yim, editors, JFPL'92, 1ères Journées Francophones de Programmation Logique, 25-27 Mai 1992, Lille, France, page 51, 1992. Google Scholar
  2. Mario Alviano, Carmine Dodaro, Johannes Klaus Fichte, Markus Hecher, Tobias Philipp, and Jakob Rath. Inconsistency proofs for ASP: the ASP - DRUPE format. Theory Pract. Log. Program., 19(5-6):891-907, 2019. URL: https://doi.org/10.1017/S1471068419000255.
  3. Haniel Barbosa, Andrew Reynolds, Gereon Kremer, Hanna Lachnitt, Aina Niemetz, Andres Nötzli, Alex Ozdemir, Mathias Preiner, Arjun Viswanathan, Scott Viteri, Yoni Zohar, Cesare Tinelli, and Clark W. Barrett. Flexible proof production in an industrial-strength SMT solver. In Jasmin Blanchette, Laura Kovács, and Dirk Pattinson, editors, Automated Reasoning - 11th International Joint Conference, IJCAR 2022, Haifa, Israel, August 8-10, 2022, Proceedings, volume 13385 of Lecture Notes in Computer Science, pages 15-35. Springer, 2022. URL: https://doi.org/10.1007/978-3-031-10769-6_3.
  4. Jeremias Berg, Bart Bogaerts, Jakob Nordström, Andy Oertel, and Dieter. Vandesande. Certified core-guided MaxSAT solving. In Proceedings of the 29th International Conference on Automated Deduction (CADE), 2023. Accepted for publication. Google Scholar
  5. Christian Bessiere. Constraint propagation. In Francesca Rossi, Peter van Beek, and Toby Walsh, editors, Handbook of Constraint Programming, volume 2 of Foundations of Artificial Intelligence, pages 29-83. Elsevier, 2006. URL: https://doi.org/10.1016/S1574-6526(06)80007-6.
  6. Christian Bessiere, Clément Carbonnel, Martin C. Cooper, and Emmanuel Hebrard. Complexity of Minimum-Size Arc-Inconsistency Explanations. In Christine Solnon, editor, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022), volume 235 of Leibniz International Proceedings in Informatics (LIPIcs), pages 9:1-9:14, Dagstuhl, Germany, 2022. Schloss Dagstuhl - Leibniz-Zentrum für Informatik. URL: https://doi.org/10.4230/LIPIcs.CP.2022.9.
  7. Bart Bogaerts, Emilio Gamba, and Tias Guns. A framework for step-wise explaining how to solve constraint satisfaction problems. Artif. Intell., 300:103550, 2021. URL: https://doi.org/10.1016/j.artint.2021.103550.
  8. Bart Bogaerts, Stephan Gocht, Ciaran McCreesh, and Jakob Nordström. Certified symmetry and dominance breaking for combinatorial optimisation. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, pages 3698-3707. AAAI Press, 2022. URL: https://ojs.aaai.org/index.php/AAAI/article/view/20283.
  9. Jens Claes, Bart Bogaerts, Rocsildes Canoy, Emilio Gamba, and Tias Guns. Zebratutor: Explaining how to solve logic grid puzzles. In Katrien Beuls, Bart Bogaerts, Gianluca Bontempi, Pierre Geurts, Nick Harley, Bertrand Lebichot, Tom Lenaerts, Gilles Louppe, and Paul Van Eecke, editors, Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019, volume 2491 of CEUR Workshop Proceedings. CEUR-WS.org, 2019. URL: http://ceur-ws.org/Vol-2491/demo96.pdf.
  10. Jens Claes, Bart Bogaerts, Emilio Gamba, Rocsildes Canoy, and Tias Guns. Human-oriented solving and explaining of logic grid puzzles, November 2019. BNAIC 2019 ; Conference date: 07-11-2019 Through 08-11-2019. Google Scholar
  11. Broes De Cat, Bart Bogaerts, Maurice Bruynooghe, Gerda Janssens, and Marc Denecker. Declarative logic programming. In Michael Kifer and Yanhong Annie Liu, editors, Declarative Logic Programming: Theory, Systems, and Applications, chapter Predicate Logic As a Modeling Language: The IDP System, pages 279-323. Association for Computing Machinery and Morgan & Claypool, New York, NY, USA, 2018. URL: https://doi.org/10.1145/3191315.3191321.
  12. Jo Devriendt. Exact solver, 2023. URL: https://gitlab.com/JoD/exact.
  13. William Dumez, Simon Vandevelde, and Joost Vennekens. Step-wise explanations of sudokus using IDP. In BNAIC/BeNeLearn, November 2022. Google Scholar
  14. Jan Elffers and Jakob Nordström. Divide and conquer: Towards faster pseudo-Boolean solving. In Jérôme Lang, editor, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, pages 1291-1299. ijcai.org, 2018. URL: https://doi.org/10.24963/ijcai.2018/180.
  15. Emilio Gamba, Bart Bogaerts, and Tias Guns. Efficiently explaining CSPs with unsatisfiable subset optimization. In Zhi-Hua Zhou, editor, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pages 1381-1388. International Joint Conferences on Artificial Intelligence Organization, August 2021. Main Track. URL: https://doi.org/10.24963/ijcai.2021/191.
  16. Martin Gebser, Benjamin Kaufmann, and Torsten Schaub. Conflict-driven answer set solving: From theory to practice. Artif. Intell., 187:52-89, 2012. URL: https://doi.org/10.1016/j.artint.2012.04.001.
  17. Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael A. Specter, and Lalana Kagal. Explaining explanations: An overview of interpretability of machine learning. In Francesco Bonchi, Foster J. Provost, Tina Eliassi-Rad, Wei Wang, Ciro Cattuto, and Rayid Ghani, editors, 5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018, Turin, Italy, October 1-3, 2018, pages 80-89. IEEE, 2018. URL: https://doi.org/10.1109/DSAA.2018.00018.
  18. Stephan Gocht, Ciaran McCreesh, and Jakob Nordström. An auditable constraint programming solver. In Christine Solnon, editor, 28th International Conference on Principles and Practice of Constraint Programming, CP 2022, July 31 to August 8, 2022, Haifa, Israel, volume 235 of LIPIcs, pages 25:1-25:18. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. URL: https://doi.org/10.4230/LIPIcs.CP.2022.25.
  19. Tias Guns. Increasing modeling language convenience with a universal n-dimensional array, cppy as python-embedded example. In Proceedings of the 18th workshop on Constraint Modelling and Reformulation at CP (Modref 2019), volume 19, 2019. Google Scholar
  20. Tias Guns, Milan Pesa, Maxime Mulamba, Ignace Bleukx, Emilio Gamba, and Senne Berden. Sudoku assistant - an AI-powered app to help solve pen-and-paper sudokus, 2022. Google Scholar
  21. Sharmi Dev Gupta, Begum Genc, and Barry O'Sullivan. Explanation in constraint satisfaction: A survey. In Zhi-Hua Zhou, editor, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021, pages 4400-4407. ijcai.org, 2021. URL: https://doi.org/10.24963/ijcai.2021/601.
  22. Sharmi Dev Gupta, Begum Genc, and Barry O'Sullivan. Finding counterfactual explanations through constraint relaxations. CoRR, abs/2204.03429, 2022. URL: https://doi.org/10.48550/arXiv.2204.03429.
  23. Pieter Van Hertum, Ingmar Dasseville, Gerda Janssens, and Marc Denecker. The KB paradigm and its application to interactive configuration. Theory Pract. Log. Program., 17(1):91-117, 2017. URL: https://doi.org/10.1017/S1471068416000156.
  24. Marijn J. H. Heule. Proofs of unsatisfiability. In Armin Biere, Marijn Heule, Hans van Maaren, and Toby Walsh, editors, Handbook of Satisfiability - Second Edition, volume 336 of Frontiers in Artificial Intelligence and Applications, pages 635-668. IOS Press, 2021. URL: https://doi.org/10.3233/FAIA200998.
  25. Alexey Ignatiev, Alessandro Previti, Mark H. Liffiton, and João Marques-Silva. Smallest MUS extraction with minimal hitting set dualization. In Gilles Pesant, editor, Principles and Practice of Constraint Programming - 21st International Conference, CP 2015, Cork, Ireland, August 31 - September 4, 2015, Proceedings, volume 9255 of Lecture Notes in Computer Science, pages 173-182. Springer, 2015. URL: https://doi.org/10.1007/978-3-319-23219-5_13.
  26. Ulrich Junker. Quickxplain: Conflict detection for arbitrary constraint propagation algorithms. In IJCAI’01 Workshop on Modelling and Solving problems with constraints, volume 4. Citeseer, 2001. Google Scholar
  27. Pat Langley, Ben Meadows, Mohan Sridharan, and Dongkyu Choi. Explainable agency for intelligent autonomous systems. In Satinder Singh and Shaul Markovitch, editors, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA, pages 4762-4764. AAAI Press, 2017. URL: http://aaai.org/ocs/index.php/IAAI/IAAI17/paper/view/15046.
  28. Niklas Lauffer and Ufuk Topcu. Human-understandable explanations of infeasibility for resource-constrained scheduling problems. In ICAPS 2019 Workshop XAIP, 2019. Google Scholar
  29. Kevin Leo and Guido Tack. Debugging unsatisfiable constraint models. In Domenico Salvagnin and Michele Lombardi, editors, Integration of AI and OR Techniques in Constraint Programming - 14th International Conference, CPAIOR 2017, Padua, Italy, June 5-8, 2017, Proceedings, volume 10335 of Lecture Notes in Computer Science, pages 77-93. Springer, 2017. URL: https://doi.org/10.1007/978-3-319-59776-8_7.
  30. Mark H. Liffiton, Alessandro Previti, Ammar Malik, and João Marques-Silva. Fast, flexible MUS enumeration. Constraints An Int. J., 21(2):223-250, 2016. URL: https://doi.org/10.1007/s10601-015-9183-0.
  31. Mark H. Liffiton and Karem A. Sakallah. Algorithms for computing minimal unsatisfiable subsets of constraints. J. Autom. Reason., 40(1):1-33, 2008. URL: https://doi.org/10.1007/s10817-007-9084-z.
  32. João Marques-Silva. Minimal unsatisfiability: Models, algorithms and applications (invited paper). In 40th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2010, Barcelona, Spain, 26-28 May 2010, pages 9-14. IEEE Computer Society, 2010. URL: https://doi.org/10.1109/ISMVL.2010.11.
  33. João Marques-Silva. Logic-based explainability in machine learning. In Leopoldo E. Bertossi and Guohui Xiao, editors, Reasoning Web. Causality, Explanations and Declarative Knowledge - 18th International Summer School 2022, Berlin, Germany, September 27-30, 2022, Tutorial Lectures, volume 13759 of Lecture Notes in Computer Science, pages 24-104. Springer, 2022. URL: https://doi.org/10.1007/978-3-031-31414-8_2.
  34. Olga Ohrimenko, Peter J. Stuckey, and Michael Codish. Propagation via lazy clause generation. Constraints An Int. J., 14(3):357-391, 2009. URL: https://doi.org/10.1007/s10601-008-9064-x.
  35. Stephen Ostermiller. QQwing. URL: https://qqwing.com/.
  36. Laurent Perron and Vincent Furnon. Or-tools, November 2022. URL: https://developers.google.com/optimization/.
  37. Francesca Rossi, Peter van Beek, and Toby Walsh, editors. Handbook of Constraint Programming, volume 2 of Foundations of Artificial Intelligence. Elsevier, 2006. URL: https://www.sciencedirect.com/science/bookseries/15746526/2.
  38. Christian Schulte and Peter J. Stuckey. Efficient constraint propagation engines. ACM Trans. Program. Lang. Syst., 31(1):2:1-2:43, 2008. URL: https://doi.org/10.1145/1452044.1452046.
  39. Ilankaikone Senthooran, Matthias Klapperstück, Gleb Belov, Tobias Czauderna, Kevin Leo, Mark Wallace, Michael Wybrow, and Maria Garcia de la Banda. Human-centred feasibility restoration. In Laurent D. Michel, editor, 27th International Conference on Principles and Practice of Constraint Programming, CP 2021, Montpellier, France (Virtual Conference), October 25-29, 2021, volume 210 of LIPIcs, pages 49:1-49:18. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021. URL: https://doi.org/10.4230/LIPIcs.CP.2021.49.
  40. Eric Taillard. Benchmarks for basic scheduling problems. European journal of operational research, 64(2):278-285, 1993. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact