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- W1567884420 abstract "One topic that is likely to attract an increasing amount of attention within the Knowledge-base systems resesearch community is the coordination of information provided by multiple experts. We envision a situation in which several experts independently encode information as belief networks. A potential user must then coordinate the conclusions and recommendations of these networks to derive some sort of consensus. One approach to such a consensus is the fusion of the contributed networks into a single, consensus model prior to the consideration of any case-specific data (specific observations, test results). This approach requires two types of combination procedures, one for probabilities, and one for graphs. Since the combination of probabilities is relatively well understood, the key barriers to this approach lie in the realm of graph theory. This paper provides formal definitions of some of the operations necessary to effect the necessary graphical combinations, and provides complexity analyses of these procedures. The paper's key result is that most of these operations are NP- hard, and its primary message is that the derivation of good consensus networks must be done heuristically." @default.
- W1567884420 created "2016-06-24" @default.
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- W1567884420 date "1993-01-01" @default.
- W1567884420 modified "2023-09-27" @default.
- W1567884420 title "Some Complexity Considerations in the Combination of Belief Networks**Supported in part by the National Science Foundation under grant SES-9106440." @default.
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- W1567884420 doi "https://doi.org/10.1016/b978-1-4832-1451-1.50023-8" @default.
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