Learning Interpretable Classifiers for PDDL Planning

• URL: http://arxiv.org/abs/2410.10011v1
• Date: Sun, 13 Oct 2024 21:12:45 GMT
• Title: Learning Interpretable Classifiers for PDDL Planning
• Authors: Arnaud Lequen,
• Abstract summary: We consider the problem of interpretable models that recognize the behaviour of an agent compared to other agents, on a set of similar planning tasks expressed in PDDL. Our approach consists in learning logical formulas, from a small set of examples that show how an agent solved small planning instances. We show that learning such formulas is computationally intractable, as it is an NP-hard problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We consider the problem of synthesizing interpretable models that recognize the behaviour of an agent compared to other agents, on a whole set of similar planning tasks expressed in PDDL. Our approach consists in learning logical formulas, from a small set of examples that show how an agent solved small planning instances. These formulas are expressed in a version of First-Order Temporal Logic (FTL) tailored to our planning formalism. Such formulas are human-readable, serve as (partial) descriptions of an agent's policy, and generalize to unseen instances. We show that learning such formulas is computationally intractable, as it is an NP-hard problem. As such, we propose to learn these behaviour classifiers through a topology-guided compilation to MaxSAT, which allows us to generate a wide range of different formulas. Experiments show that interesting and accurate formulas can be learned in reasonable time.

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