Retro-engineering state machines into PDDL domains
Résumé
This paper presents a new algorithm based on grammar induction, called AMLSI (Action Model Learning with State machine Interactions), that retro-engineers planning domains described with Planning Problem Description Language (PDDL) by querying a state machine with action sequences and by observing the state transitions. AMLSI takes as input a training set of feasible and infeasible action sequences built from partial observations and returns a PDDL domain. A key issue for domain learning is the ability to plan with the learned domains. It often happens that a small learning error leads to a domain that is unusable for planning. Unlike other algorithms, we show that AMLSI is able to lift this lock by learning domains from partial observations with sufficient accuracy to allow planners to solve new problems.
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