Abstract

We explore the problem of step-wise explaining how to solve constraint satisfaction problems, with a use case on logic grid puzzles. More specifically, we study the problem of explaining the inference steps that one can take during propagation, in a way that is easy to interpret for a person. Thereby, we aim to give the constraint solver explainable agency, which can help in building trust in the solver by being able to understand and even learn from the explanations. The main challenge is that of finding a sequence of simple explanations, where each explanation should aim to be as cognitively easy as possible for a human to verify and understand. This contrasts with the arbitrary combination of facts and constraints that the solver may use when propagating. We propose the use of a cost function to quantify how simple an individual explanation of an inference step is, and identify the explanation-production problem of finding the best sequence of explanations of a CSP. Our approach is agnostic of the underlying constraint propagation mechanisms, and can provide explanations even for inference steps resulting from combinations of constraints. In case multiple constraints are involved, we also develop a mechanism that allows to break the most difficult steps up and thus gives the user the ability to zoom in on specific parts of the explanation. Our proposed algorithm iteratively constructs the explanation sequence by using an optimistic estimate of the cost function to guide the search for the best explanation at each step. Our experiments on logic grid puzzles show the feasibility of the approach in terms of the quality of the individual explanations and the resulting explanation sequences obtained.
Original languageEnglish
Article number103550
JournalArtificial Intelligence
Volume300
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Artificial Intelligence
  • constraint satisfaction
  • explanation
  • Explainable AI
  • MUS
  • Natural language processing
  • Logic programming
  • explanatory framework

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  • Efficiently Explaining CSPs with Unsatisfiable Subset Optimization

    Gamba, E., Bogaerts, B. & Guns, T., 2021, Efficiently Explaining CSPs with Unsatisfiable Subset Optimization . IJCAI, p. 1381-1388 191

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  • Step-wise Explanations of Constraint Satisfaction Problems

    Bogaerts, B., Gamba, E., Guns, T. & Claes, J., 24 Aug 2020, ECAI 2020 - 24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings. De Giacomo, G., Catala, A., Dilkina, B., Milano, M., Barro, S., Bugarin, A. & Lang, J. (eds.). IOS Press, Vol. 325. p. 640-647 8 p. (Frontiers in Artificial Intelligence and Applications; vol. 325).

    Research output: Chapter in Book/Report/Conference proceedingConference paper

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    2 Citations (Scopus)
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  • ZebraTutor: Explaining how to solve logic grid puzzles (demo)

    Claes, J., Bogaerts, B., Canoy, R., Gamba, E. & Guns, T., 1 Jan 2019, Proceedings of the 31st Benelux Conference on Artificial Intelligence (demos). Vol. 2491. p. 96-96 1 p. (CEUR Workshop Proceedings).

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