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Probability Concepts for an Expert System Used for Data Fusion

AI Magazine

Probability concepts for ruled-based expert systems are developed that are compatible with probability used in data fusion of imprecise information. Confidence limits are defined as being proportional to root-mean-square errors in estimates, and a method is outlined that allows the confidence limits in the probability estimate of the hypothesis to be expressed in terms of the confidence limits in the estimate of the evidence. Procedures are outlined for weighting and combining multiple reports that pertain to the same item of evidence. The illustrative examples apply to tactical data fusion, but the same probability procedures can be applied to other expert systems.


Integrating knowledge sources for computer understanding tasks

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