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Citation: Eric Horvitz and John S. Breese and Max Henrion. (1988) Decision Theory in Expert Systems and Artificial Intelligence. In Journal of Approximate Reasoning, Special Issue on Uncertainty in Artificial Intelligence, ,,247-302,1988.
| Publication article ( Edit )
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| type | Article
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| bibtype | article
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| Bibtex basics
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| author | Eric Horvitz and John S. Breese and Max Henrion
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| title | Decision Theory in Expert Systems and Artificial Intelligence
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| journal | Journal of Approximate Reasoning, Special Issue on Uncertainty in Artificial Intelligence
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| pages | 247-302
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| year | 1988
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| Bibtex more
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| note | Postscript of this article can be found at http://www.research.microsoft.com/research/dtg/horvitz/DT.HTM.
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| Access Paper
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| 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 withing the expert systems setting, yet are crucial for developing theoretical methods and computational architectures for automated reasoners.
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| KSL Technical Report ID: KSL-88-13
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Facts about Decision Theory in Expert Systems and Artificial IntelligenceRDF feed
| Abstract | Despite their different perspectives, arti … 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 withing the expert systems setting, yet are crucial for developing theoretical methods and computational architectures for automated reasoners. nal architectures for automated reasoners. |
| Author | Eric Horvitz and John S. Breese and Max Henrion + |
| Bibtype | article + |
| Has author | Eric Horvitz and John S. Breese and Max Henrion + |
| Has identifier | KSL-88-13 + |
| Has publishing details | ,,247-302,1988 + |
| Has title | Decision Theory in Expert Systems and Artificial Intelligence + |
| Has where published | Journal of Approximate Reasoning, Special Issue on Uncertainty in Artificial Intelligence + |
| Has year | 1988 + |
| Journal | Journal of Approximate Reasoning, Special Issue on Uncertainty in Artificial Intelligence + |
| Ksl tr id | KSL-88-13 + |
| Note | Postscript of this article can be found at http://www.research.microsoft.com/research/dtg/horvitz/DT.HTM. |
| Pages | 247-302 + |
| Process note | GOOGLE + |
| Title | Decision Theory in Expert Systems and Artificial Intelligence + |
| Year | 1988 + |
