An Accurate PDDL Domain Learning Algorithm from Partial and Noisy Observations - Grand, Maxence; Pellier, Damien; Fiorino, Humbert (Proceedings - International Conference on Tools with Artificial Intelligence, TAI, 2022)
This paper presents a novel approach to learn PDDL domain called AMLSI (Action Model Learning with State machine Interaction) based on grammar induction. AMLSI learns with no prior knowledge from a training dataset made up of action sequences built by random walks and by observing state transitions. The domain learnt is accurate enough to be used without human proofreading in a planner even with very highly partial and noisy observations. Thus AMLSI takles a key issue for domain learning that is the ability to plan with the learned domains. It often happens that small learning errors lead to domains that are unusable for planning. AMLSI contribution is to learn domains from partial and noisy observations with sufficient accuracy to allow planners to solve new problems. Compared to other approaches, AMLSI uses smaller training datasets and exploits both feasible and infeasible generated action sequences.