Uncertainty in Artificial Intelligence, Volume 1Laveen N. Kanal, John F. Lemmer Hardbound. How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context. |
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Malcolm Bauer Bell Communications Research Morristown NJ 07974 | 3 |
R K Bhatnagar and L N Kanal 3 | 18 |
Consensus Rules | 25 |
Copyright | |
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absolute belief approach argument Artificial Intelligence assume assumptions axioms balls Bayes Bayesian Bayesian probability Bel H E belief functions belief update calculus certainty factor model change in belief combination function combination rule Computer conditional independence conditional probabilities conflict consensus constraints corresponding decision defined degree of belief Dempster-Shafer theory Dempster's Rule E₁ E₂ equation example expert systems Figure formalism frame of discernment fuzzy logic fuzzy numbers Fuzzy Sets given graph H₁ hypothesis inconsistent influence diagram knowledge label likelihood ratio mathematical maximum entropy measures of belief method modularity monotonic MYCIN nodes non-monotonic logics operator p(Age parallel combination pieces of evidence possible posterior prior prior probabilities probabilistic interpretation probabilistic logic probabilistic reasoning probability distribution probability theory problem propagation properties proposition PROSPECTOR relative entropy represent representation result satisfies semantics sensor sequential combination Shafer Shortliffe subset T-norms term set tion uncertain uncertainty variable Zadeh