Planning is one of the most important and oldest fields of AI. However, there is no consensus on how to compare and classify planning systems and methods. Neither the traditional view of planning as search nor the formalization efforts have been able to provide a basis for a classification scheme.This article explores the idea that a perspective based on Newell's knowledge level can be useful for this task. We present a knowledge-level analysis of classical planning systems in terms of models of the problem-solving methods they used. Rather than reengineering these systems in detail, however, our goal is to show how this type of analysis can help define which roles knowledge may play in planning tasks, and how these roles can be used to compare planning methods in terms of (i) which types of knowledge are used, (ii) how they are structured in what we call domain models. As a tool to analyze and represent planning methods we use the KADS methodology.
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Knowledge-level analysis of planning systems
Valente, André
Follow ACM SIGART Bulletin
, Volume 6 (1)
Association for Computing Machinery – Jan 11, 1995
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