A flexible and fault tolerant query-reply system based on a Bayesian neural network.
A query-reply system based on a Bayesian neural network is described. Strategies for generating questions which make the system both efficient and highly fault tolerant are presented. This involves having one phase of question generation intended to quickly reach a hypothesis followed by a phase where verification of the hypothesis is attempted. In addition, both phases have strategies for detecting and removing inconsistencies in the replies from the user. Also described is an explanatory mechanism which gives information related to why a certain hypotheses is reached or question asked. Specific examples of the systems behavior as well as the results of a statistical evaluation are presented.
Holst
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Department of Numerical Analysis and Computing Science, Royal Institute of Technology, Stockholm, Sweden.
Lansner
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D000818:Animals; D001499:Bayes Theorem; D007256:Information Systems; D008959:Models, Neurological; D016571:Neural Networks, Computer