  This paper presents new approach based on grammar induction called AMLSI
Action Model Learning with State machine Interactions. The AMLSI approach does
not require a training dataset of plan traces to work. AMLSI proceeds by trial
and error: it queries the system to learn with randomly generated action
sequences, and it observes the state transitions of the system, then AMLSI
returns a PDDL domain corresponding to the system. 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 and noisy observations with sufficient accuracy to allow
planners to solve new problems.
