Knowledge Strata Reactive Planning with a Multi-level Architecture

Abstract

This report demonstrates the use of "multi-level" or "layered" knowledge representation in Artificial Intelligence planning systems. Although multi-level representation schemes have been in use since the earliest days of AI, certain principles and advantages of knowledge stratification have never been made fully explicit. In this paper we examine issues of multi-level knowledge representation in the context of "reactive planning systems"; that is, in systems which extend the applicability of AI planning systems to complex, dynamic domains. The complexity and real-time requirements of reactive planning have lead several researchers to propose multi-level approaches. Our aim is to improve upon the state of the art in reactive planning by bringing to bear an analysis of the principles of multi-level event and action representation. Our work has lead to the implementation of a prototype architecture (called APE, for Abstraction-Partitioned Evaluator) and, within this architecture, a reactive planner (HomeBot) which operates in a household task domain.