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An Automata-Based Constraint Programming Framework for Optimal Classical Planning

Authors Damien Van Meerbeeck , Arnaud Lequen , Gilles Pesant , Jendrik Seipp

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Subject Classification

ACM Subject Classification
  • Computing methodologies → Planning and scheduling
Keywords
  • Optimal Classical Planning
  • Landmark Constraints
  • Automata

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Abstract

Recent work has shown that classical planning tasks can be compactly factored into deterministic finite automata and solved optimally with constraint programming (CP). In this setting, finding a plan reduces to finding a word accepted by all automata through Regular constraints. So far, however, these automata have had to be carefully handcrafted from PDDL tasks. In this paper, we show that they can instead be generated automatically and used as the basis of CP models. We also show that the resulting framework is easily extensible with additional constraints from the planning literature that strengthen propagation. Our approach solves more tasks than the state of the art in end-to-end CP for classical planning in almost all domains.

Cite As Get BibTex

Damien Van Meerbeeck, Arnaud Lequen, Gilles Pesant, and Jendrik Seipp. An Automata-Based Constraint Programming Framework for Optimal Classical Planning. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 55:1-55:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.CP.2026.55

Author Details

Damien Van Meerbeeck
  • Linköping University, Sweden
Arnaud Lequen
  • Linköping University, Sweden
Gilles Pesant
  • Polytechnique Montréal, Canada
Jendrik Seipp
  • Linköping University, Sweden

Funding

This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. Computations were performed at NSC Tetralith provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) funded by the Swedish Research Council through grant agreement no. 2022-06725.

Supplementary Materials

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