Introduction: Revising, Updating and Combining Knowledge; D. Dubois, H. Prade. Revision of Belief Sets and Belief Bases; S.O. Hansson. How Hard is it to Revise a Belief Base? B. Nebel. Conditionals and the Ramsey Test; S. Lindström, W. Rabinowicz. Logics for Belief Base Updating; A. Herzig. Reasoning about Merged Information; L. Cholvy. Numerical Representations of Uncertainty; P. Smets. Belief Change Rules in Ordinal and Numerical Uncertainty Theories; D. Dubois, S. Moral, H. Prade. Parallel Combination of Information Sources; J. Gebhardt, R. Kruse. Index.

Belief change is an emerging field of artificial intelligence and information science dedicated to the dynamics of information and the present book provides a state-of-the-art picture of its formal foundations. It deals with the addition, deletion and combination of pieces of information and, more generally, with the revision, updating and fusion of knowledge bases. The book offers an extensive coverage of, and seeks to reconcile, two traditions in the kinematics of belief that often ignore each other - the symbolic and the numerical (often probabilistic) approaches. Moreover, the work encompasses both revision and fusion problems, even though these two are also commonly investigated by different communities. Finally, the book presents the numerical view of belief change, beyond the probabilistic framework, covering such approaches as possibility theory, belief functions and convex gambles.
The work thus presents a unified view of belief change operators, drawing from a widely scattered literature embracing philosophical logic, artificial intelligence, uncertainty modelling and database systems. The material is a clearly organised guide to the literature on the dynamics of epistemic states, knowledge bases and uncertain information, suitable for scholars and graduate students familiar with applied logic, knowledge representation and uncertain reasoning.