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Simplifying Step-Wise Explanation Sequences

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

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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 AsGet 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

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