Learning partially observable action schemas

We present an algorithm that derives actions' effects and preconditions in partially observable, relational domains. Our algorithm has two unique features: an expressive relational language, and an exact tractable computation. An action-schema language that we present permits learning of preconditions and effects that include implicit objects and unstated relationships between objects. For example, we can learn that replacing a blown fuse turns on all the lights whose switch is set to on. The algorithm maintains and outputs a relational-logical representation of all possible action-schema models after a sequence of executed actions and partial observations. Importantly, our algorithm takes polynomial time in the number of time steps and predicates. Time dependence on other domain parameters varies with the action-schema language. Our experiments show that the relational structure speeds up both learning and generalization, and outperforms propositional learning methods. It also allows establishing apriori-unknown connections between objects (e.g. light bulbs and their switches), and permits learning conditional effects in realistic and complex situations. Our algorithm takes advantage of a DAG structure that can be updated efficiently and preserves compactness of representation.