Decision Theory in Expert Systems and Artificial Intelligence

Eric Horvitz, John Breese, Max Henrion

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Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decision-theoretic framework. Recent analyses of the restrictions of several traditional AI reasoning techniques, coupled with the development of more tractable and expressive decision-theoretic representation and inference strategies, has stimulated renewed interest in decision theory and decision analysis. We describe early experience with simple probabilistic schemes for automated reasoning, review the dominant expert-system paradigm, and survey some recent research at the crossroads of AI and decision science. In particular, we present the belief network and influence diagram representations. Finally, we discuss issues that have not been studied in detail within the expert systems setting, yet are crucial for developing theoretical methods and computational architectures for automated reasoners. (54 pages)

Keywords: Decision theory, Bayesian networks, Bayesian methods, rationality under bounded resources, decision analysis, influence diagrams, decision-theoretic inference, probabilistic inference, expert systems, explanation.

In: Journal of Approximate Reasoning, Special Issue on Uncertainty in Artificial Intelligence, 2:247-302. Also, Stanford CS Technical Report KSL-88-13.

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