Heuristic reasoning about uncertainty: an artificial intelligence approach
Heuristic reasoning about uncertainty: an artificial intelligence approach
Fusion, propagation, and structuring in belief networks
Artificial Intelligence
A statistical view of uncertainty in expert systems
Artificial intelligence and statistics
Uncertainty in artificial intelligence: Is probability epistemologically and heuristically accurate?
Expert judgment and expert systems
Intelligent decision systems (decision analysis, artificial intelligence, infertility, gynecology, urology)
An empirical study of how visual programming aids in comprehending quantitative policy models. (volumes i and ii) (user interfaces, non-procedural languages, software psychology)
An experimental comparison of uncertain inference systems (artificial intelligence, probability, entropy)
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Causal understanding of patient illness in medical diagnosis
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
A Bayesian method for constructing Bayesian belief networks from databases
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
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Decision analysis provides a set of techniques for structuring and encoding expert knowledge, comparable with knowledge engineering techniques for rule-based expert systems. In order to compare the expert systems and decision analysis approach, each was applied to the same task, namely the diagnosis and treatment of root disorders in apple trees. This experiment illustrates a variety of theoretical and practical differences between them, including the semantics of the network representations (inference net vs. influence diagram or Bayes' belief net), approaches to modelling uncertainty and preferences, the relative effort required, and their attitudes to human reasoning under uncertainty, as the ideal to be emulated or as unreliable and to be improved upon.