First-order logic is the traditional basis for knowledge representation languages. However, its applicability to many real-world tasks is limited by its inability to represent uncertainty. Bayesian belief networks, on the other hand, are inadequate for complex KR tasks due to the limited expressivity of the underlying (propositional) language. The need to incorporate uncertainty into an expressive language has led to a resurgence of work on first-order probabilistic logic. This paper addresses one of the main objections to the incorporation of probabilities into the language: "Where do the numbers come from?" We present an approach that takes a knowledge base in an expressive rule-based first-order language, and learns the probabilistic parameters associated with those rules from data cases. Our approach, which is based on algorithms for learning in traditional Bayesian networks, can handle data cases where many of the relevant aspects of the situation are unobserved. It is also capabl...

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We present a coding scheme, based on a Bayesian Network (BN) formalism, for describing probabilistic and causal information in arguments in medical genetics. The scheme was applied to a corpus of genetic counseling letters and evaluated for intercoder reliability. Results show that the model is highly relevant to the corpus while intercoder reliability of the coding scheme is good. We plan to use the coding scheme in an empirical study of argument strategies. Since the coding scheme refers only to BN concepts and general concepts in medical diagnosis, it may be useful to other researchers for empirical studies of natural language corpora in medicine. 1

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First-order logic is the traditional basis for knowledge representation languages. However, its applicability to many real-world tasks is limited by its inability to represent uncertainty. Bayesian belief networks, on the other hand, are inadequate for complex KR tasks due to the limited expressivity of the underlying (prepositional) language. The need to incorporate uncertainty into an expressive language has led to a resurgence of work on first-order probabilistic Logic. This paper addresses one of the main objections to the incorporation of probabilities into the language: "Where do the numbers come from? " We present an approach that takes a knowledge base in an expressive rule-based first-order language, and leams the probabilistic parameters associated with those rules from data cases. Our approach, which is based on algorithms for learning in traditional Bayesian networks, can handle data cases where many of the relevant aspects of the situation are unobserved. It is also capable of utilizing a rich variety of data cases, including instances with varying causal structure, and even involving a varying number of individuals. These features allow the approach to be used for a wide range of tasks, such as learning genetic propagation models or learning first-order STRIPS planning operators with uncertain effects. 1

genes confer susceptibility to breast and ovarian cancer. At least 7 models for estimating the probabilities of having a mutation are used widely in clinical and scientific activities; however, the merits and limitations of these models are not fully understood. Objective: To systematically quantify the accuracy of the following publicly available models to predict mutation carrier status: BRCAPRO, family history assessment tool, Finnish, Myriad, National Cancer Institute, University of Pennsylvania, and Yale University. Design: Cross-sectional validation study, using model predictions and BRCA1 or BRCA2 mutation status of patients different from those used to develop the models. Setting: Multicenter study across Cancer Genetics Network partic-ipating centers. Patients: 3 population-based samples of participants in research studies and 8 samples from genetic counseling clinics.