Acquiring qualitative and quantitative knowledge from verbal statements and dialogues in probabilistic domains

We describe an approach to acquire qualitative and quantitative knowledge from verbal statements and dialogues in complex, probabilistic domains. This work is part of the development of an intelligent environment, MEDICUS (Modelling, explanation, and diagnostic support for complex, uncertain subject matters), that supports modelling and diagnostic reasoning in the domains of environmental medicine and human genetics. The system is designed for professional as well as for further education purposes in these two medical domains. Support for other domains of rapidly changing and uncertain knowledge will be possible as well. In MEDICUS, uncertainty is handled by the Bayesian network approach. Thus modelling consists of creating a Bayesian network for the problem at hand. Since MEDICUS is designed for users interested in the domain but not necessarily in mathematical issues, it is possible to state propositions verbally and let the system generate a Bayesian network proposal. This differs from existing reasoning systems based on Bayesian networks, i.e. in medical domains, which contain a built-in knowledge base that may be used but not created or modified by the user. Diagnostic reasoning and deciding consists of using the network for stating and testing diagnostic hypotheses, and asking for recommendations.