On defaults in action theories

We study the integration of two prominent fields of logic-based AI: action formalisms and non-monotonic reasoning. The resulting framework allows an agent employing an action theory as internal world model to make useful default assumptions. We show that the mechanism behaves properly in the sense that all intuitively possible conclusions can be drawn and no implausible inferences arise. In particular, it suffices to make default assumptions only once (in the initial state) to solve projection problems.