Mathematical Models for Handling Partial Knowledge in Artificial Intelligence

Chapter 1 Ellsberg Paradox Intuition and Choquet Expected Utility

Chapter 2 Fuzzy Logic as Logic

Chapter 3 Mathematical Foundations of Evidence Theory

Chapter 4 Semantics for Uncertain Inference Based on Statistical Knowledge

Chapter 5 Prospects and Problems in Applying the Fundamental Theorem of Prevision as an Expert System: An Example of Learning About Parole Decisions

Chapter 6 Coherent Prevision as a Linear Functional without an Underlying Measure Space: The Purely Arithmetic Structure of Logical Relations Among Conditional Quantities

Chapter 7 Revision Rules for Convex Sets of Probabilities

Chapter 8 Some Mathematical Tools for Decision Making under Partial Knowledge

Chapter 9 From Bayesian Networks to Causal Networks

Chapter 10 Generalized Concept of Atoms for Conditional Events

Chapter 11 Checking the Coherence of Conditional Probabilities in Expert Systems: Remarks and Algorithms

Chapter 12 A Hyperstructure of Conditional Events for Artificial Intelligence

Chapter 13 Possibilistic Logic and Plausible Inference

Chapter 14 Probability Logic as a Fuzzy Logic

Chapter 15 Algorithms for Precise and Imprecise Conditional Probability Assessments

Chapter 16 A Valuation-Based Architecture for Assumption-Based Reasoning

Chapter 17 Computing Symbolic Support Functions by Classical Theorem-Proving Techniques

Chapter 18 Inconsistent Knowledge Integration in a Probabilistic Model

Chapter 19 Conditional and Comparative Probabilities in Artificial Intelligence

Chapter 20 Roundtable (Panel discussion)