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AMLSI: A Novel Accurate Action Model Learning Algorithm
[article]
2020
arXiv
pre-print
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
arXiv:2011.13277v1
fatcat:epdg76a2jvbrpebxcn6o2pjkn4