Comparative Analysis of Frameworks for Knowledge-Intensive Intelligent Agents

Randolph M. Jones and Robert E. Wray

This paper discusses representations and processes for agents and behavior models that encode large knowledge bases, are long-lived, and exhibit high degrees of competence and flexibility while interacting with complex environments. There are many different approaches to building such agents, and understanding the important commonalities and differences between aproaches is often difficult. We introduce a new approach to comparing approaches based on the notions of deliberate commitment, reconsideration, and a categorization of representations. We review three agent frameworks, concentrating on the major representations and processes each directly supports. By organizing the approaches according to a common nomenclature, the analysis highlights points of similarity and difference and suggests directions for integrating and unifying disparate approaches and for incorporating research results from one approach into alternative ones.

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