Pseudo-Boolean Proof Logging for Optimal Classical Planning

We introduce lower-bound certificates for classical planning tasks, which can be used to prove the unsolvability of a task or the optimality of a plan in a way that can be verified by an independent third party. We describe a general framework for generating lower-bound certificates based on pseudo-Boolean constraints, which is agnostic to the planning algorithm used. As a case study, we show how to modify the A* algorithm to produce proofs of optimality with modest overhead, using pattern database heuristics and hmax as concrete examples. The same proof logging approach works for any heuristic whose inferences can be efficiently expressed as reasoning over pseudo-Boolean constraints.

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Journal
Proceedings of the International Conference on Automated Planning and Scheduling
Published
2025-09-16
DOI
https://doi.org/10.1609/icaps.v35i1.36101
Primary Topic
Logic, programming, and type systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Pseudo-Boolean Proof Logging for Optimal Classical Planning

Jakob Nordstr”öm, Gabriele Röger, Malte Helmert, Simon Dold et al.
Proceedings of the International Conference on Automated Planning and Scheduling
Logic, programming, and type systems
article

Pseudo-Boolean Proof Logging for Optimal Classical Planning

Jakob Nordstr”öm, Gabriele Röger, Malte Helmert, Simon Dold, Tanja Schindler
article en

Abstract

We introduce lower-bound certificates for classical planning tasks, which can be used to prove the unsolvability of a task or the optimality of a plan in a way that can be verified by an independent third party. We describe a general framework for generating lower-bound certificates based on pseudo-Boolean constraints, which is agnostic to the planning algorithm used. As a case study, we show how to modify the A* algorithm to produce proofs of optimality with modest overhead, using pattern database heuristics and hmax as concrete examples. The same proof logging approach works for any heuristic whose inferences can be efficiently expressed as reasoning over pseudo-Boolean constraints.

Proceedings of the International Conference on Automated Planning and SchedulingVol. 35(1)
University of Copenhagen (DK), University of Basel (CH), Lund University (SE)
National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Openalex Percentile: Top 87%
Logic, programming, and type systems
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