KR2020Proceedings of the 17th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning

Rhodes, Greece. September 12-18, 2020.

Edited by
  1. Diego Calvanese
  2. Esra Erdem
  3. Michael Thielscher

ISSN: 2334-1033
ISBN: 978-0-9992411-7-2

Sponsored by
Published by

Copyright © 2020 International Joint Conferences on Artificial Intelligence Organization

Explainable Planning Using Answer Set Programming

  1. Van Nguyen(New Mexico State University)
  2. Stylianos Loukas Vasileiou(Washington University in St Louis)
  3. Tran Cao Son(New Mexico State University)
  4. William Yeoh(Washington University in St. Louis)
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BibTeX
DOI 10.24963/kr.2020/66

Keywords

  1. Explanation finding, diagnosis, causal reasoning, abduction-General
  2. Applications of KR-General
  3. Logic programming, answer set programming, constraint logic programming-General

Abstract

In human-aware planning problems, the planning agent may need to explain its plan to a human user, especially when the plan appears infeasible or suboptimal for the user. A popular approach to do so is called model reconciliation, where the planning agent tries to reconcile the differences between its model and the model of the user such that its plan is also feasible and optimal to the user. This problem can be viewed as an optimization problem, where the goal is to find a subset-minimal explanation that one can use to modify the model of the user such that the plan of the agent is also feasible and optimal to the user. This paper presents an algorithm for solving such problems using answer set programming.