Operations Research
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
Comparison of Rule-Based and Bayesian Network Approaches in Medical Diagnostic Systems
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Updating beliefs with incomplete observations
Artificial Intelligence
The Influence of Influence Diagrams in Medicine
Decision Analysis
A probabilistic plan recognition algorithm based on plan tree grammars
Artificial Intelligence
Conservative inference rule for uncertain reasoning under incompleteness
Journal of Artificial Intelligence Research
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The process of diagnosis involves learning about the state of a system from various observations of symptoms or findings about the system. Sophisticated Bayesian (and other) algorithms have been developed to revise and maintain beliefs about the system as observations are made. Nonetheless, diagnostic models have tended to ignore some common sense reasoning exploited by human diagnosticians. In particular, one can learn from which observations have not been made, in the spirit of conversational implicature. In order to extract information from the observations not made, we propose the following two concepts. First, some symptoms, if present, are more likely to be reported before others. Second. most human diagnosticians and expert systems are economical in their data-gathering, searching first where they are more likely to find symptoms present. Thus, there is a desirable bias toward reporting symptoms that are present. We develop a simple model for these concepts that can significantly improve diagnostic inference.