Schröder, Olaf and Möbus, Claus and Thole, Heinz-Jürgen (1996) Acquiring Knowledge from Linguistic Models in Complex, Probabilistic Domains. In: Proceedings of the European Conference on Artificial Intelligence in Education (EuroAI-ED ´96). Edições Colibri, Lisbon, Portugal, pp. 206-212. ISBN 972-8288-37-9

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This paper describes an approach to acquire qualitative and quantitative knowledge from verbally stated models in complex, probabilistic domains. This work is part of the development of an intelligent environment, MEDICUS, that supports modelling and diagnostic reasoning in the domains of environmental medicine and human genetics. These domains are two yet new subdomains of medicine receiving increasing research efforts, but still consisting of largely fragile and uncertain knowledge. In MEDICUS, uncertainty is handled by the Bayesian network approach. Thus the modelling task for the user consists of creating a Bayesian network for the problem at hand. But since we want mathematically untrained persons to work with MEDICUS, the user may alternatively 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. The diagnostic reasoning task for the learner consists of using the network for stating diagnostic goals, and for proposing diagnostic hypotheses and examinations.

Item Type: Book Section
Uncontrolled Keywords: qualitative and quantitative knowledge, verbal stated models, complex probabilistic domains, MEDICUS, modelling and knowledge acquisition, Bayesian network, diagnostic reasoning, environmental medicine and human genetics, naive user, system-generated network proposals, diagnostic reasoning task, stating diagnostic goals, proposing diagnostic hypotheses, proposing diagnostic examinations
Subjects: Generalities, computers, information > Computer science, internet
Philosophy and psychology > Psychology
Divisions: School of Computing Science, Business Administration, Economics and Law > Department of Computing Science
Date Deposited: 09 Sep 2015 11:28
Last Modified: 09 Sep 2015 11:28
URN: urn:nbn:de:gbv:715-oops-22831

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