Safe Learning of PDDL Domains with Conditional Effects -- Extended Version
This addresses a bottleneck in automated planning for AI agents by enabling safe learning in domains with conditional effects, though it is incremental as it builds on prior safe learning methods.
The paper tackles the challenge of automatically learning safe action models for planning domains with conditional effects, proving that such learning can require exponential samples but proposing the first tractable algorithm, Conditional-SAM, which solves most test problems in experimented domains.
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.