Learning partially observable action models: Efficient algorithms

We present tractable, exact algorithms for learning actions' effects and preconditions in partially observable domains. Our algorithms maintain a propositional logical representation of the set of possible action models after each observation and action execution. The algorithms perform exact learning of preconditions and effects in any deterministic action domain. This includes STRIPS actions and actions with conditional effects. In contrast, previous algorithms rely on approximations to achieve tractability, and do not supply approximation guarantees. Our algorithms take time and space that are polynomial in the number of domain features, and can maintain a representation that stays compact indefinitely. Our experimental results show that we can learn efficiently and practically in domains that contain over 1000's of features (more than 21¹⁰⁰⁰ states).