Homomorphisms and Embeddings of STRIPS Planning Models

ABSTRACT Determining whether two STRIPS planning instances are isomorphic is the simplest form of comparison between planning instances. It is also a particular case of the problem concerned with finding an isomorphism between a planning instance and a sub‐instance of another instance . One application of such a mapping is to efficiently produce a compiled form containing all solutions to from a compiled form containing all solutions to . We also introduce the notion of embedding from an instance to another instance , which allows us to deduce that has no solution‐plan if is unsolvable. In this paper, we study the complexity of these problems. We show that the first is GI‐complete and can thus be solved, in theory, in quasi‐polynomial time. While we prove the remaining problems to be NP‐complete, we propose an algorithm to build an isomorphism when possible. We report extensive experimental trials on benchmark problems that demonstrate conclusively that applying constraint propagation in preprocessing can greatly improve the efficiency of a SAT solver.

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Publication Details

Journal
Computational Intelligence
Published
2024-12-01
DOI
https://doi.org/10.1111/coin.70013
Citations
1
Primary Topic
AI-based Problem Solving and Planning
Type
article
Field-Weighted Citation Impact
0.35
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article

Homomorphisms and Embeddings of STRIPS Planning Models

Martin Cooper, Frédéric Maris, Arnaud Lequen
1 citations
Computational Intelligence
AI-based Problem Solving and Planning
0.35
article

Homomorphisms and Embeddings of STRIPS Planning Models

Martin Cooper, Frédéric Maris, Arnaud Lequen
article en
1 citations

Abstract

ABSTRACT Determining whether two STRIPS planning instances are isomorphic is the simplest form of comparison between planning instances. It is also a particular case of the problem concerned with finding an isomorphism between a planning instance and a sub‐instance of another instance . One application of such a mapping is to efficiently produce a compiled form containing all solutions to from a compiled form containing all solutions to . We also introduce the notion of embedding from an instance to another instance , which allows us to deduce that has no solution‐plan if is unsolvable. In this paper, we study the complexity of these problems. We show that the first is GI‐complete and can thus be solved, in theory, in quasi‐polynomial time. While we prove the remaining problems to be NP‐complete, we propose an algorithm to build an isomorphism when possible. We report extensive experimental trials on benchmark problems that demonstrate conclusively that applying constraint propagation in preprocessing can greatly improve the efficiency of a SAT solver.

Computational IntelligenceVol. 40(6)
Université Toulouse III - Paul Sabatier (FR), Université Toulouse-I-Capitole (FR), Institut de Recherche en Informatique de Toulouse (FR), Université Toulouse - Jean Jaurès (FR), Institut Polytechnique de Bordeaux (FR)
Sustainable cities and communities
Openalex Percentile: Top 31%
AI-based Problem Solving and Planning
0.35
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