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Training on the haptic VR simulator and conventional phantom head had equivalent effects on minimizing procedural errors in endodontic access cavity preparation.

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We present a method for dynamically gen erating Bayesian networks from knowledge bases consisting of first-order probability logic sentences. We present a subset of proba bility logic sufficient for representing the class of Bayesian networks with discrete-valued nodes. We impose constraints on the form of the sentences that guarantee that the knowl edge base contains all the probabilistic infor mation necessary to generate a network. We define the concept of d-separation for knowl edge bases and prove that a knowledge base with independence conditions defined by d separation is a complete specification of a probability distribution. We present a net work generation algorithm that, given an in ference problem in the form of a query Q and a set of evidence E, generates a network to compute P(QIE). We prove the algorithm to be correct.

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