Generalized planning as heuristic search

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• dc.contributor.author Segovia-Aguas, Javier
• dc.contributor.author Jiménez, Sergio
• dc.contributor.author Jonsson, Anders, 1973-
• dc.date.accessioned 2023-02-07T13:23:40Z
• dc.date.available 2023-02-07T13:23:40Z
• dc.date.issued 2021
• dc.description Comunicació presentada a ICAPS2021, celebrat del 2 a 13 d'agost de 2021 de manera virtual.
• dc.description.abstract Although heuristic search is one of the most successful approaches to classical planning, this planning paradigm does not apply straightforwardly to Generalized Planning (GP). Planning as heuristic search traditionally addresses the computation of sequential plans by searching in a grounded state-space. On the other hand, GP aims at computing algorithm-like plans that can branch and loop, and that generalize to a (possibly infinite) set of planning instances. This paper adapts the {\em planning as heuristic search} paradigm to the particularities of GP and presents the first native heuristic search approach to GP. First, the paper defines a novel GP solution space that is independent of the number of planning instances in a GP problem, and the size of these instances. Second, the paper defines several evaluation and heuristic functions, that do not require grounding, for guiding a combinatorial search in the GP solution space, making it possible to handle state variables with large numerical domains (e.g. integers). Lastly, the paper defines an algorithm for GP called Best-First Generalized Planning (BFGP), that implements a best-first search in the solution space guided by our evaluation/heuristic functions.
• dc.description.sponsorship Javier Segovia-Aguas is supported by TAILOR, a project funded by EU H2020 research and innovation programme no. 952215, an ERC Advanced Grant no. 885107, and grant TIN-2015-67959-P from MINECO, Spain. Sergio Jiménez is supported by the Ramon y Cajal program, RYC-2015-18009, the Spanish MINECO project TIN2017-88476-C2-1-R. Anders Jonsson is partially supported by Spanish grants PID2019-108141GB-I00 and PCIN-2017-082.
• dc.format.mimetype application/pdf
• dc.identifier.citation Segovia-Aguas J, Jimenez Celorrio S, Jonsson A. Generalized planning as heuristic search. In: Biundo S, Do M, Goldman R, Katz M, Yang Q, Zhuo HH, editors. Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling (ICAPS2021); 2021 Aug 2-13; Guangzhou, China. California: AAAI Press; 2021. p. 569-77. DOI: 10.1609/icaps.v31i1.16005
• dc.identifier.doi http://dx.doi.org/10.1609/icaps.v31i1.16005
• dc.identifier.issn 2334-0835
• dc.identifier.uri http://hdl.handle.net/10230/55666
• dc.language.iso eng
• dc.publisher Association for the Advancement of Artificial Intelligence (AAAI)
• dc.relation.ispartof Biundo S, Do M, Goldman R, Katz M, Yang Q, Zhuo HH, editors. Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling (ICAPS2021); 2021 Aug 2-13; Guangzhou, China. California: AAAI Press; 2021. p. 569-77.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/952215
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN-2015-67959-P
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/TIN2017-88476-C2-1-R
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-108141GB-I00
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PCIN-2017-082
• dc.rights © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org)
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Learning Effective Heuristics And Other Forms Of Control Knowledge
• dc.subject.keyword Learning To Improve The Effectiveness Of Planning & Scheduling Systems
• dc.title Generalized planning as heuristic search
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion