Uncertain Information Processing In Expert SystemsUncertain Information Processing in Expert Systems systematically and critically examines probabilistic and rule-based (compositional, MYCIN-like) systems, the two most important families of expert systems dealing with uncertainty. The book features a detailed introduction to probabilistic systems (including methods using graphical models and methods of knowledge integration), an analysis of compositional systems based on algebraic considerations, an application of graphical models, and the Dempster-Shafer theory of evidence and its use in expert systems. The book will be useful to anyone working in artificial intelligence, statistical computing, symbolic logic, and expert systems. |
Contents
Probability | 7 |
5 | 27 |
3 | 47 |
4 | 54 |
Decision Making Under Uncertainty | 67 |
2 | 77 |
Local Computations with Probabilities on Graphical Structures | 85 |
Knowledge Integration Methods | 113 |
3 | 158 |
6 | 164 |
An Algebraic Analysis | 175 |
A Probabilistic Analysis of Compositional Systems | 195 |
The DempsterShafer Theory of Evidence and Its | 219 |
Estimation of Probabilities and Structures | 255 |
169 | 275 |
| 283 | |
Common terms and phrases
A₁ abelian group algebraic algorithm Artificial Intelligence assume assumption axiom B(HE Bayesian Bayesian d-pairs belief functions Boolean bp assignment C₁ causal graph cl(H Clearly combining functions compositional systems Compstat computations conditional independence conditional probability conditional probability tables consider corresponding d-potential decision function decomposable defined Definition Dempster-Shafer theory Dempster's denote edges entropy estimate example expert systems Figure focal element formula frame given global weight Goal graph G H₁ H₂ Hájek Havránek inference input knowledge isomorphic joint probability distribution Lemma log-linear models m₁ m₂ mapping marginal Markov property maximal Möbius transform MYCIN nodes nonextremal obtained optimal ordering P₁ P₂ potentials probabilistic probability theory Proof proposition Ques question questionnaire random field rule base running intersection property S₁ Section semigroup Shannon entropy subsets system of cliques Theorem three-valued triangulated uncertainty V₁ vertex vertices