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Integrating Artificial Intelligence and Decision Theory in Heuristic Process Control Systems

Thomas J. Watson IBM Research CenterD. A. KleinStanford University. Computer Science Department. Knowledge Systems LaboratoryEdward Hance Shortliffe

出版	Knowledge Systems Laboratory, Stanford University, 1990
URL	http://books.google.com.hk/books?id=ITzMHgAACAAJ&hl=&source=gbs_api

註釋

Abstract: "Choices among competing process-control actions often involve tradeoffs. In this paper, we describe a formal, decision-theoretic approach to automating such choices in the context of heuristic process control systems. We demonstrate the potential benefits of this approach with respect to reasoning, explanation, and knowledge acquisition, employing examples from the domain of computer-operations management. Although experimental, our methodology suggests that decision theory potentially provides an effective model for reasoning about preferences and tradeoffs in decision-intensive heuristic process control systems."