Mathematical Models for Handling Partial Knowledge in Artificial Intelligence

Edited by Giulianella Coletti, Didier Dubois and Romano Scozzafava

in Springer Books from Springer

Date: 1995
ISBN: 978-1-4899-1424-8
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Chapters in this book:

Ellsberg Paradox Intuition and Choquet Expected Utility

Alain Chateauneuf

Fuzzy Logic as Logic

Petr Hájek

Mathematical Foundations of Evidence Theory

Jürg Kohlas

Semantics for Uncertain Inference Based on Statistical Knowledge

Henry E. Kyburg

Prospects and Problems in Applying the Fundamental Theorem of Prevision as an Expert System: An Example of Learning About Parole Decisions

Frank Lad and Ian Coope

Coherent Prevision as a Linear Functional without an Underlying Measure Space: The Purely Arithmetic Structure of Logical Relations Among Conditional Quantities

Frank Lad

Revision Rules for Convex Sets of Probabilities

Serafín Moral and Nic Wilson

Some Mathematical Tools for Decision Making under Partial Knowledge

Hung T. Nguyen

From Bayesian Networks to Causal Networks

Judea Pearl

Generalized Concept of Atoms for Conditional Events

Andrea Capotorti

Checking the Coherence of Conditional Probabilities in Expert Systems: Remarks and Algorithms

Giuseppe Di Biase and Antonio Maturo

A Hyperstructure of Conditional Events for Artificial Intelligence

Serena Doria and Antonio Maturo

Possibilistic Logic and Plausible Inference

Didier Dubois and Henri Prade

Probability Logic as a Fuzzy Logic

Giangiacomo Gerla

Algorithms for Precise and Imprecise Conditional Probability Assessments

Angelo Gilio

A Valuation-Based Architecture for Assumption-Based Reasoning

Rolf Haenni

Computing Symbolic Support Functions by Classical Theorem-Proving Techniques

Urs Hänni

Inconsistent Knowledge Integration in a Probabilistic Model

Radim Jiroušek and Jiří Vomlel

Conditional and Comparative Probabilities in Artificial Intelligence

Paolo Vicig

Roundtable (Panel discussion)

Romano Scozzafava

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DOI: 10.1007/978-1-4899-1424-8

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