Decision Theory in Expert Systems and Artificial Intelligence
Eric Horvitz, John Breese, Max Henrion
Access postscript or pdf formats.
Abstract:
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.
Author Email:
horvitz@microsoft.com, breese@microsoft.com, henrion@lumina.com
Back to Eric Horvitz's home page.