Towards Defeasible Reasoning about Actions and Their Effects

Abstract

Intelligent agents interacting with their environment need to reason about the effects their actions will have on their surroundings. For actions with deterministic outcome this problem is well investigated in the context of planning problems. But many real-world actions may not always result in their typical outcome, and exceptional outcomes of an action have to be taken into account. In this work we present a framework for reasoning about actions with uncertain outcomes. Inspired by conditionals, we introduce defeasible action rules that capture the typical outcome of action, without committing to what happens in exceptional circumstances. For the semantics of these rules we introduce ranked transition schemas: graph-based representations of transitions between possible worlds in which edges between states are assigned a rank indicating their typicality. We define an inference operator for defeasible action rules inspired by rational closure, adapting its intuitive semantics to the dynamic setting. Furthermore, we explore a generalization of this framework that uses partial orderings over transitions instead of ranks. This generalization strictly increases expressive power and enables more refined reasoning operators. Finally, we extend the language of defeasible action rules with a notation to express that certain properties are not affected by the execution of an action, thus allowing to explicitly formulate frame axioms in this defeasible setting.