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Publication Details

A Call for Knowledge-based Planning

by Wilkins, D. E. and desJardins, M.

AI Magazine, vol. 22, no. 1, pp. 99-115, Spring 2001.

Abstract

We are interested in solving real-world planning problems and, to that end, argue for the use of domain knowledge in planning. We believe that the field must develop methods capable of using rich knowledge models in order to make planning tools useful for complex problems. We discuss the suitability of current planning paradigms for solving these problems. In particular, we compare knowledge-rich approaches such as hierarchical task network (HTN) planning to minimal-knowledge methods such as STRIPS-based planners and disjunctive planners (DPs). We argue that the former methods have advantages such as scalability, expressiveness, continuous plan modification during execution, and the ability to interact with humans. However, these planners also have limitations, such as requiring complete domain models and failing to model uncertainty, that often make them inadequate for real-world problems. In this paper, we define the terms knowledge-based (KB) and primitive-action (PA) planning, and argue for the use of KB planning as a paradigm for solving real-world problems. We next summarize some of the characteristics of real-world problems that we are interested in addressing. Several current real-world planning applications are described, focusing on the ways in which knowledge is brought to bear on the planning problem. We describe some existing KB approaches, and then discuss additional capabilities, beyond those available in existing systems, that are needed. Finally, we draw an analogy from the current focus of the planning community on disjunctive planners to the experiences of the machine learning community over the past decade.

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AIC Personnel

Name Title E-mail
desJardins, Marie Alumnus
Wilkins, David E Senior Computer Scientist

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