| Abstract
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We present strategies for explaining decis … We present strategies for explaining decision-theoretic choices automatically, and we describe the role of these strategies in "Interpretive Value Analysis," our broader framework for explanation and knowledge acquisition in expert systems that model tradeoff-intensive decisions. Our explanations are at once empirically motivated and formally sound with respect to decision theory, retaining the advantages of both artificial-intelligence and decision-theoretic representations for modeling decisions. We demonstrate the explanation strategies with implemented examples in the domains of marketing, process control, and medicine. Although previous approaches to modeling decisions in expert systems often have sacrificed formal specification for transparent operation, our methodology suggests that knowledge engineers can retain the benefits of decision theory without compromising intuitive explanation. ithout compromising intuitive explanation.
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