Qualitative Probabilistic Networks for Planning Under Uncertainty
DOI: 10.1016/B978-0-444-70396-5.50023-6
关键词:
摘要: Bayesian networks provide a probabilistic semantics for qualitative assertions about likelihood. A reasoner based on an algebra over these can derive further conclusions the influence of actions. While are much weaker than those computed from complete probability distributions, they still valuable suggesting potential actions, eliminating obviously inferior plans, identifying important tradeoffs, and explaining models.
elsevier.com
arxiv.org 
sci-hub.st 参考文章(14)
Michael P. Wellman, Probabilistic semantics for qualitative influences national conference on artificial intelligence. pp. 660- 664 ,(1987)
M. Chapman Findlay, G. A. Whitmore, Stochastic dominance : an approach to decision-making under risk Lexington Books. ,(1978)
M. P. Wellman, REASONING ABOUT PREFERENCE MODELS Massachusetts Institute of Technology. ,(1985)
Paul Raymond Cohen, None, Heuristic reasoning about uncertainty: an artificial intelligence approach Pitman Publishing, Inc.. ,(1984)
Peter C. Fishburn, Analysis of Decisions with Incomplete Knowledge of Probabilities Operations Research. ,vol. 13, pp. 217- 237 ,(1965) , 10.1287/OPRE.13.2.217
Sholom M. Weiss, Casimir A. Kulikowski, Saul Amarel, Aran Safir, A model-based method for computer-aided medical decision-making Artificial Intelligence. ,vol. 11, pp. 145- 172 ,(1978) , 10.1016/0004-3702(78)90015-2
Benjamin Kuipers, Commonsense reasoning about causality: deriving behavior from structure Artificial Intelligence. ,vol. 24, pp. 169- 203 ,(1984) , 10.1016/0004-3702(84)90039-0
Ross D. Shachter, Evaluating Influence Diagrams Operations Research. ,vol. 34, pp. 871- 882 ,(1986) , 10.1287/OPRE.34.6.871
Judea Pearl, Fusion, propagation, and structuring in belief networks Artificial Intelligence. ,vol. 29, pp. 241- 288 ,(1986) , 10.1016/0004-3702(86)90072-X