title: Generating explanations and tutorial problems from Bayesian networks.
abstract: We present a system that generates explanations and tutorial problems from the probabilistic information contained in Bayesian belief networks. BANTER is a tool for high-level interaction with any Bayesian network whose nodes can be classified as hypotheses, observations, and diagnostic procedures. Users need no knowledge of Bayesian networks, only familiarity with the particular domain and an elementary understanding of probability. Users can query the knowledge base, identify optimal diagnostic procedures, and request explanations. We describe BANTER's algorithms and illustrate its application to an existing medical model.
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee.
authors.forename: P
authors.initials: P
authors.lastname: Haddawy
authors.forename: J
authors.initials: J
authors.lastname: Jacobson
authors.forename: C E
authors.initials: CE
authors.lastname: Kahn
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003194:Computer-Assisted Instruction; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted; D005705:Gallbladder Diseases / Q000175:diagnosis; D006801:Humans; D016571:Neural Networks, Computer