Abstract
A minimal connection model of abductive diagnostic reasoning is presented. The domain knowledge is represented by a causal network. An explanation of a set of observations is a chain of causation events. These causation events constitute a scenario where all the observations can be observed. The authors define the best explanation to be the most probable explanation. The underlying causal model enables one to compute the probabilities of explanations from the conditional probabilities of the participating causation events. An algorithm for finding the most probable explanations is presented. Although probabilistic inference using belief networks is NP-hard in general, this algorithm is polynomial to the number of nodes in the networks and is exponential only to the number of observations to be explained, which, in any single case, is usually small. >
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
