Efficiently Explaining CSPs with Unsatisfiable Subset Optimization

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Abstract

Recently, a novel method for explaining solutions of constraint satisfaction problems was proposed.
An explanation in that method is a sequence of simple inference steps, where the simplicity of an inference step depends on the number and types of constraints used, eventually explaining all logical consequences of the problem.
The current paper tackles two questions left in builds on these formal foundations and tackles two trailing questions: namely how to generate explanations that are provably optimal (with respect to the given cost metric) and how to generate them efficiently.
To answer these questions, we develop 1) an implicit hitting set algorithm for finding optimal unsatisfiable subsets; 2) a method to reduce multiple calls for (optimal) unsatisfiable subsets to a single call that takes constraints on the subset into account, and 3) a method for re-using relevant information over multiple calls to these algorithms.
Original languageEnglish
Title of host publication Efficiently Explaining CSPs with Unsatisfiable Subset Optimization
EditorsZhi-Hua Zhou
PublisherIJCAI
ChapterMain Track
Pages1381-1388
Number of pages8
ISBN (Electronic)978-0-9992411-9-6
DOIs
Publication statusPublished - 2021
Event30th International Joint Conference on Artificial Intelligence (IJCAI-21): IJCAI-21 - Canada, Montreal, Canada
Duration: 21 Aug 202126 Aug 2021
https://ijcai-21.org/

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference30th International Joint Conference on Artificial Intelligence (IJCAI-21)
Abbreviated titleIJCAI 2021
CountryCanada
CityMontreal
Period21/08/2126/08/21
Internet address

Keywords

  • Weighted Unsatisfiable Subsets
  • constraint satisfaction
  • Algorithm
  • Explainable AI
  • Artificial intelligence

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