Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains

Abstract

Building a knowledge base requires iterative refinement to correct imperfections that keep lurking after each new version of the system. This paper concentrates on the automatic refinement of incomplete domain models for planning systems, presenting both a methodology for addressing the problem and empirical results. Planning knowledge may be refined automatically through direct interaction with the environment. Missing conditions cause unreliable predictions of action outcomes. Missing effects cause unreliable predictions of facts about the state. We present a practical approach based on continuous and selective interaction with the environment that pinpoints the type of fault in the domain knowledge that causes any unexpected behavior of the environment, and resorts to experimentation when additional information is needed to correct the fault. Our approach has been implemented in EXPO, a system that uses PRODIGY as a baseline planner and improves its domain knowledge in several domains when initial domain knowledge is up to 50% incomplete. The empirical results presented show that EXPO dramatically improves its prediction accuracy and reduces the amount of unreliable action outcomes.