Mathematical Models for Handling Partial Knowledge in Artificial Intelligence - Rilegato

 
9780306450761: Mathematical Models for Handling Partial Knowledge in Artificial Intelligence

Sinossi

Petr Hajek shows that reasoning in fuzzy logic may be put on a strict logical (formal) basis, so contributing to our understanding of what fuzzy logic is and what one is doing when applying fuzzy reasoning.

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Contenuti

Invited Papers: Ellsberg Paradox Intuition and Choquet Expected Utility (A. Chateauneuf). Fuzzy Logic as Logic (P. Hájek). Mathematical Foundations of Evidence Theory (J. Kohlas). Semantics for Uncertain Inference Based on Statistical Knowledge (H.E. Kyburg). Prospects and Problems in Applying the Fundamental Theorem of Prevision as an Expert System: An Example of Learning about Parole Decisions (F. Lad, I. Coope). Coherent Prevision as a Linear Functional without an Underlying Measure Space: The Purely Arithmetic Structure of Logical Relations among Conditional Quantities (F. Lad). Revision Rules for Convex Sets of Probabilities (S. Moral, N. Wilson). Contributed Papers: Generalized Concept of Atoms for Conditional Events (A. Capotorti). Checking the Coherence of Conditional Probabilities in Expert Systems: Remarks and Algorithms (G. Di Biase, A. Maturo). A Hyperstructure of Conditional Events for Artificial Intelligence (S. Doria, A. Maturo). Possibilistic Logic and Plausible Inference (D. Dubois, H. Prade). Probability Logic as a Fuzzy Logic (G. Gerla). Algorithms for Precise and Imprecise Conditional Probability Assessments (A. Gilio). A Valuationbased Architecture for Assumptionbased Reasoning (R. Haenni). 6 additional articles. Index.

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9781489914262: Mathematical Models for Handling Partial Knowledge in Artificial Intelligence

Edizione in evidenza

ISBN 10:  1489914269 ISBN 13:  9781489914262
Casa editrice: Springer, 2013
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