Abstract
The aim of the paper is to formally relate logical Horn models and Bayesian Networks (BNs) in the framework of diagnostic reasoning. This is pursued by pointing out similarities between the two formalisms at the modeling level and by introducing into BNs a suitable notion of derivation. We also discuss modeling issues underlying the choice of Horn-based models vs BNs, by making explicit the “completion semantics” underlying a BN. This correspondence between “completed” Horn theories and BNs allows us to formally justify classical diagnostic schemata adopted for BNs.
The work has been partially supported by CNR project SCI*SIA.
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Portinale, L., Torasso, P. (1997). A comparative analysis of Horn models and Bayesian Networks for diagnosis. In: Lenzerini, M. (eds) AI*IA 97: Advances in Artificial Intelligence. AI*IA 1997. Lecture Notes in Computer Science, vol 1321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63576-9_113
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DOI: https://doi.org/10.1007/3-540-63576-9_113
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