2026-07-22T07:05:29Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:20697 2024-08-29T08:52:52Z ddc:004 open_access

Using Constraint Programming for Disjunctive Scheduling in Temporal AI Planning Francis Green, Adam Beck, J. Christopher Coles, Amanda AI Planning Temporal-Numeric Planning Constraint Programming Scheduling We present a novel scheduling model that leverages Constraint Programming (CP) to enhance problem solving performance in Temporal Planning. Building on the established strategy of decomposing causal and temporal reasoning, our approach abstracts two common fact structures present in many Temporal Planning problems - Semaphores and Envelopes - and performs temporal reasoning in a CP-based scheduler. At each search node in a heuristic search for a temporal plan, we construct and solve a Constraint Satisfaction Problem (CSP) and integrate feedback from the CP-based scheduler to guide the causal planning search towards a solution. Through experimental analysis, we validate the impact of these advances, demonstrating a significant reduction in both the number of states searched and in search time alongside an increase in problem-solving coverage. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Adam Francis Green and J. Christopher Beck and Amanda Coles 2024 Is Part Of LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.CP.2024.12 urn:nbn:de:0030-drops-206974 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.12 eng https://creativecommons.org/licenses/by/4.0/legalcode