Generating explanations and tutorial problems from Bayesian networks

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.