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Determining Action Reversibility in STRIPS Using Answer Set and Epistemic Logic Programming

Published online by Cambridge University Press:  27 September 2021

WOLFGANG FABER Open the ORCID record for WOLFGANG FABER [Opens in a new window] ,
MICHAEL MORAK Open the ORCID record for MICHAEL MORAK [Opens in a new window]  and
LUKÁŠ CHRPA
WOLFGANG FABER
Affiliation:
University of Klagenfurt, Klagenfurt, Austria (e-mails: wolfgang.faber@aau.at, michael.morak@aau.at)
MICHAEL MORAK
Affiliation:
University of Klagenfurt, Klagenfurt, Austria (e-mails: wolfgang.faber@aau.at, michael.morak@aau.at)
LUKÁŠ CHRPA
Affiliation:
Czech Technical University in Prague, Prague, Czech Republic (e-mail: chrpaluk@fel.cvut.cz)
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Abstract

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In the context of planning and reasoning about actions and change, we call an action reversible when its effects can be reverted by applying other actions, returning to the original state. Renewed interest in this area has led to several results in the context of the PDDL language, widely used for describing planning tasks. In this paper, we propose several solutions to the computational problem of deciding the reversibility of an action. In particular, we leverage an existing translation from PDDL to Answer Set Programming (ASP), and then use several different encodings to tackle the problem of action reversibility for the STRIPS fragment of PDDL. For these, we use ASP, as well as Epistemic Logic Programming (ELP), an extension of ASP with epistemic operators, and compare and contrast their strengths and weaknesses.

Keywords

action reversibilityAnswer Set ProgrammingEpistemic Logic Programmingreasoning about action and change
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 21 , Issue 5: 37th International Conference on Logic Programming Special Issue I , September 2021 , pp. 646 - 662
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Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Footnotes

*

This work is based on, and significantly extends, two workshop papers previously published by the authors at ASPOCP’20 and EELP’20 (Chrpa et al. 2020; Faber and Morak 2020).

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