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Contrastive Explanations for Answer-Set Programs

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Logics in Artificial Intelligence (JELIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14281))

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Abstract

Answer-Set Programming (ASP) is a popular declarative reasoning and problem solving formalism. Due to the increasing interest in explainability, several explanation approaches have been developed for ASP. However, while those formalisms are correct and interesting on their own, most are more technical and less oriented towards philosophical or social concepts of explanation. In this work, we study the notion of contrastive explanation, i.e., answering questions of the form “Why P instead of Q?”, in the context of ASP. In particular, we are interested in answering why atoms are included in an answer set, whereas others are not. Contrastive explainability has recently become popular due to its strong support from the philosophical, cognitive, and social sciences and its apparent ability to provide explanations that are concise and intuitive for humans. We formally define contrastive explanations for ASP based on counterfactual reasoning about programs. Furthermore, we demonstrate the usefulness of the concept on example applications and give some complexity results. The latter also provide a guideline as to how the explanations can be computed in practice.

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Acknowledgments

This work was supported by funding from the Bosch Center for Artificial Intelligence. Furthermore, Tobias Geibinger is a recipient of a DOC Fellowship of the Austrian Academy of Sciences at the Institute of Logic and Computation at the TU Wien.

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Authors and Affiliations

  1. Knowledge-based Systems Group, Institute for Logic and Computation, TU Wien, Vienna, Austria

    Thomas Eiter, Tobias Geibinger & Johannes Oetsch

Authors
  1. Thomas Eiter
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  2. Tobias Geibinger
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  3. Johannes Oetsch
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Correspondence to Tobias Geibinger .

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Editors and Affiliations

  1. TU Dresden, Dresden, Germany

    Sarah Gaggl

  2. Artificial Intelligence Research Institute - IIIA CSIC, Barcelona, Spain

    Maria Vanina Martinez

  3. Umeå University, Umeå, Sweden

    Magdalena Ortiz

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Eiter, T., Geibinger, T., Oetsch, J. (2023). Contrastive Explanations for Answer-Set Programs. In: Gaggl, S., Martinez, M.V., Ortiz, M. (eds) Logics in Artificial Intelligence. JELIA 2023. Lecture Notes in Computer Science(), vol 14281. Springer, Cham. https://doi.org/10.1007/978-3-031-43619-2_6

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  • DOI: https://doi.org/10.1007/978-3-031-43619-2_6

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