From Causal Theories to Successor State Axioms and STRIPS-like Systems

Abstract

We describe a system for specifying the efiects of actions. Unlike those commonly used in AI planning, our system uses an action description language that allows one to specify the efiects of actions using domain rules, which are state constraints that can entail new action efiects from old ones. Declaratively, an action domain in our language corresponds to a nonmonotonic causal theory in the situation calculus. Procedurally, such an action domain is compiled into a set of propositional theories, one for each action in the domain, from which fully instantiated successor state-like axioms and STRIPS-like systems are then generated. We expect the system to be a useful tool for knowledge engineers writing action speciflcations for classical AI planning systems, GOLOG systems, and other systems where formal speciflcations of actions are needed.