Compiling Causal Theories to Successor State Axioms and STRIPS-Like Systems

We describe a system for specifying the effects of actions. Unlike those commonly used in AI planning, our system uses an action description language that allows one to specify the effects of actions using domain rules, which are state constraints that can entail new action effects 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 logical 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 specifications for classical AI planning systems, GOLOG systems, and other systems where formal specifications of actions are needed

Deriving Invariants and Constraints from Action Theories

. Recent work on reasoning about action has shown that there exists an interesting connection between action specifications and state constraints --- it is possible to extract state constraints from action specifications. This work provides us another way to describe the behaviour of dynamic systems. In this paper, we address the problem of generating action invariants from action specifications, and generalizing action invariants into state constraints. We first propose a persistence-based formalism of actions, and show that the generation of action invariants is achieved from action specifications by reasoning about persistence. We then investigate the generalization of action invariants into state constraints. Blocks world examples illustrate the general procedure throughout. Keywords: knowledge representation, logics for artificial intelligence, reasoning about action 1. Introduction In dynamic systems (i.e., database systems, planning systems and dynamic physical systems), the sy..