Learning to reason about actions

Abstract

We focus on learning representations of dynamical systems that can be characterized by logic-based formalisms for reasoning about actions and change, where system's behaviors are naturally viewed as appropriate logical consequences of the domain's description. To this end, logic-based induction methods are adapted to identify the input/output behavior of a dynamical system corresponding to an environment. The study of dynamic domains is started with domains modelable with classical action theories and is progressively enhanced to manage more complex behaviors.