Safe Learning of PDDL Domains with Conditional Effects -- Extended Version

TL;DR

This paper introduces Conditional-SAM, the first algorithm capable of safely learning PDDL domain models with conditional effects, under certain assumptions, and demonstrates its effectiveness through theoretical analysis and experiments.

Contribution

The paper presents Conditional-SAM, a novel algorithm for safe learning of PDDL domains with conditional effects, addressing a gap in existing methods.

Findings

Conditional-SAM can learn safe action models with conditional effects.

The learned models enable solving most test problems accurately.

Learning with conditional effects may require exponential samples without assumptions.

Abstract

Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in some planning domain description language. Manually designing such an action model is a notoriously challenging task. An alternative is to automatically learn action models from observation. Such an action model is called safe if every plan created with it is consistent with the real, unknown action model. Algorithms for learning such safe action models exist, yet they cannot handle domains with conditional or universal effects, which are common constructs in many planning problems. We prove that learning non-trivial safe action models with conditional effects may require an exponential number of samples. Then, we identify reasonable assumptions under which such learning is tractable and propose SAM Learning of Conditional Effects (Conditional-SAM), the first algorithm capable of doing so. We analyze Conditional-SAM theoretically and evaluate it experimentally. Our results show that the action models learned by Conditional-SAM can be used to solve perfectly most of the test set problems in most of the experimented domains.