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Uncertainty in artificial intelligence 6

Publisher: Amsterdam ; New York : North-Holland ; New York, N.Y., U.S.A. : Distributors for the U.S. and Canada, Elsevier Science Pub. Co., 1991.
Series: Machine intelligence and pattern recognition, v. 12.
Edition/Format:   Print book : Conference publication : EnglishView all editions and formats

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Genre/Form: Conference papers and proceedings
Material Type: Conference publication
Document Type: Book
ISBN: 0444892648 9780444892645
OCLC Number: 25083521
Notes: Papers presented at the Sixth Conference on Uncertainty in Artificial Intelligence, held in July, 1990, at the Massachusetts Institute of Technology, Cambridge, Mass.
Description: xvi, 528 pages : illustrations ; 23 cm.
Contents: Qualitative Probabilistic Reasoning and Cognitive Models. Exploiting Functional Dependencies in Qualitative Probabilistic Reasoning (M.P. Wellman). Qualitative Propagation and Scenario-Based Scheme for Explaining Probabilistic Reasoning (M. Henrion, M.J. Druzdel). Propagating Uncertainty in Rule Based Cognitive Modeling (T.R. Shultz). Context-Dependent Similarity (Y. Cheng). Abductive Probabilistic Reasoning and KB Development. Similarity Networks for the Construction of Multiple-Faults Belief Networks (D. Heckerman). Separable and Transitive Graphoids (D. Geiger, D. Heckerman). Integrating Probabilistic, Taxonomic and Causal Knowledge in Abductive Diagnosis (D. Lin, R. Goebel). What is the Most Likely Diagnosis (D. Poole, G.M. Provan). Probabilistic Evaluation of Candidate Sets for Multidisorder Diagnosis (T.D. Wu). Kutato: An Entropy-Driven System for Construction of Probabilistic Expert Systems from Databases (E. Herskovits, G. Cooper). Problem Formulation and Control of Reasoning. Ideal Reformulation of Belief Networks (J.S. Breese, E.J. Horvitz). Computationally-Optimal Real-Resource Strategies for Independent, Uninterruptible Methods (D. Einav, M.R. Fehling). Problem Formulation as the Reduction of a Decision Model (D.E. Heckerman, E.J. Horvitz). Dynamic Construction of Belief Networks (R.P. Goldman, E. Charniak). A New Algorithm for Finding MAP Assignments to Belief Networks (S.E. Shimony, E. Charniak). Belief Network Decomposition. Directed Reduction Algorithms and Decomposable Graphs (R.D. Shachter, S.K. Andersen, K.L. Poh). Optimal Decomposition of Belief Networks (W.X. Wen). Pruning Bayesian Networks for Efficient Computation (M. Baker, T.E. Boult). On Heuristics for Finding Loop Cutsets in Multiply-Connected Belief Networks (J. Stillman). A Combination of Cutset Conditioning with Clique-Tree Propagation in the Pathfinder System (H.J. Suermondt, G.F. Cooper, D.E. Heckerman). Equivalence and Synthesis of Causal Models (T.S. Verma, J. Pearl). Possibility Theory: Semantics and Applications. Possibility as Similarity: The Semantics of Fuzzy Logic (E. Ruspini). Integrating Case-Based and Rule-Based Reasoning: the Possibilistic Connection (S. Dutta, P.P. Bonissone). Credibility Discounting in the Theory of Approximate Reasoning (R.R. Yager). Updating with Belief Functions, Ordinal Conditional Functions and Possibility Measures (D. Dubois, H. Prade). A Hierarchical Approach to Designing Approximate Reasoning-Based Controllers for Dynamic Physical Systems (H.R. Berenji, et al.). Dempster-Shafer: Graph Decomposition, FMT, and Interpretations. A New Approach to Updating Beliefs (R. Fagin, J.Y. Halpern). The Transferable Belief Model and Other Interpretations of Dempster-Shafer's Model (P. Smets). Valuation-Based Systems for Discrete Optimization (P.P. Shenoy). Computational Aspects of the Mobius Transformation (R. Kennes, P. Smets). Using Dempster-Shafer Theory in Knowledge Representation (A. Saffiotti).
Series Title: Machine intelligence and pattern recognition, v. 12.
Other Titles: Uncertainty in artificial intelligence six
Responsibility: edited by Piero P. Bonissone [and others].


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