Cambridge, UK: Cambridge University Press, 2006. — 224 p. — ISBN10: 9780521032728; ISBN13: 978-0521032728 — (Cambridge Tracts in Theoretical Computer Science. Book 39)
Reasoning under uncertainty, that is, making judgments with only partial knowledge, is a major theme in artificial intelligence. Professor Paris provides here an introduction to the mathematical foundations of the subject. The author presents the key results on the subject, and formalizes within a unified framework the main contemporary approaches and assumptions. He concentrates on giving clear mathematical formulations, analyses, justifications, and consequences of the main theories about uncertain reasoning.
Motivation
Belief as Probability
Justifying Belief as Probability
Dempster — Shafer Belief
Truth-functional Belief
Inference Processes
Principles of Uncertain Reasoning
Belief Revision
Independence
Computational Feasibility
Uncertain Reasoning in the Predicate Calculus
Principles of Predicate Uncertain Reasoning