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Answer Set Planning: A Survey

Published online by Cambridge University Press:  01 April 2022

SON CAO TRAN Open the ORCID record for SON CAO TRAN [Opens in a new window] ,
ENRICO PONTELLI Open the ORCID record for ENRICO PONTELLI [Opens in a new window] ,
MARCELLO BALDUCCINI  and
TORSTEN SCHAUB Open the ORCID record for TORSTEN SCHAUB [Opens in a new window]
SON CAO TRAN
Affiliation:
Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA (e-mails: tson@cs.nmsu.edu, epontell@cs.nmsu.edu)
ENRICO PONTELLI
Affiliation:
Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA (e-mails: tson@cs.nmsu.edu, epontell@cs.nmsu.edu)
MARCELLO BALDUCCINI
Affiliation:
Department of Decision and System Sciences, Saint Joseph’s University, Philadelphia, PA 19131, USA (e-mail: mbalducc@sju.edu)
TORSTEN SCHAUB
Affiliation:
Department of Computer Science, University of Potsdam, Potsdam, Germany (e-mail: torsten@cs.uni-potsdam.de)
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Abstract

Answer Set Planning refers to the use of Answer Set Programming (ASP) to compute plans, that is, solutions to planning problems, that transform a given state of the world to another state. The development of efficient and scalable answer set solvers has provided a significant boost to the development of ASP-based planning systems. This paper surveys the progress made during the last two and a half decades in the area of answer set planning, from its foundations to its use in challenging planning domains. The survey explores the advantages and disadvantages of answer set planning. It also discusses typical applications of answer set planning and presents a set of challenges for future research.

Keywords

planningknowledge representation and reasoninglogic programming

Information

Type
Survey Article
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 1 , January 2023 , pp. 226 - 298
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

*

Tran Son and Enrico Pontelli have been partially supported by NSF grants 1914635, 1833630, 1757207, and 1812628. Tran Son’s and Marcello Balduccini’s contribution was made possible in part through the help and support of NIST via cooperative agreement 70NANB21H167. Torsten Schaub was supported by DFG grant SCHA 550/15, Germany.

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