MP Wellman, JS Breese, and RP Goldman

Knowledge Engineering Review, 7:35–53, 1992.
Copyright © 1992 Cambridge University Press.

Abstract

In recent years there has been a growing interest among AI researchers in probabilistic and decision modeling, spurred by significant advances in representation and computation with network modeling formalisms. In applying these techniques to decision-support tasks, fixed network models have proven inadequately expressive to handle a broad range of situations. Therefore, many researchers have sought to combine the flexibility of general-purpose knowledge representation languages with the normative status and well-understood computational properties of decision-modeling formalisms and algorithms. One approach is to encode general knowledge in an expressive language, then dynamically construct a decision model for each particular situation or problem instance. We have developed several systems adopting this approach, which illustrate a variety of interesting techniques and design issues.

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