Uncertainty in artificial intelligence: Is probability epistemologically and heuristically accurate?
Expert judgment and expert systems
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Toward a theory of impasse-driven learning
Learning Issues for Intelligent Tutoring Systems
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
The Dempster-Shafer theory of evidence
Readings in uncertain reasoning
Unified theories of cognition
Probabilistic similarity networks
Probabilistic similarity networks
AIME '95 Proceedings of the 5th Conference on Artificial Intelligence in Medicine in Europe: Artificial Intelligence Medicine
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
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MEDICUS (modeling, explanation, and diagnostic support for complex, uncertain subject matters) is an intelligent modeling and diagnosis environment designed to support the construction of explanation models and diagnostic reasoning in domains where knowledge is complex, fragile, and uncertain. MEDICUS is developed in collaboration with several medical institutions in the epidemiological fields of environmentally caused diseases and human genetics. Uncertainty is handled by the Bayesian network approach. In modeling, the user creates a Bayesian network for the problem at hand, receiving help information and explanations from the system. 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. MEDICUS supports diagnostic reasoning by proposing diagnostic hypotheses and recommending examinations. In this paper we will focus on the modeling component of MEDICUS.