KR2021Proceedings of the 18th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning

Online event. November 3-12, 2021.

Edited by
  1. Meghyn Bienvenu
  2. Gerhard Lakemeyer
  3. Esra Erdem

ISSN: 2334-1033
ISBN: 978-1-956792-99-7

Sponsored by
Published by

Copyright © 2021 International Joint Conferences on Artificial Intelligence Organization

Correcting Hierarchical Plans by Action Deletion

  1. Roman Barták(Charles University)
  2. Simona Ondrčková(Charles University)
  3. Gregor Behnke(University of Freiburg)
  4. Pascal Bercher(Australian National University)
PDF
BibTeX
https://doi.org/10.24963/kr.2021/10

Keywords

  1. Explanation finding, diagnosis, causal reasoning, abduction
  2. Reasoning about actions and change, action languages
  3. Reasoning about constraints, constraint programming
  4. Explainable AI

Abstract

Hierarchical task network (HTN) planning is a model-based approach to planning. The HTN domain model consists of tasks and methods to decompose them into subtasks until obtaining primitive tasks (actions). There are recent methods for verifying if a given action sequence is a valid HTN plan. However, if the plan is invalid, all existing verification methods only say so without explaining why the plan is invalid. In the paper, we propose a method that corrects a given action sequence to form a valid HTN plan by deleting the minimal number of actions. This plan correction explains what is wrong with a given action sequence concerning the HTN domain model.