Conditionals, Information, and Inference : International Workshop, WCII 2002, Hagen, Germany, May 13-15, 2002, Revised Selected Papers / edited by Gabriele Kern-Isberner, Wilhelm Rödder, Friedhelm Kulmann.

Format:

Book

Contributor:

Kern-Isberner, Gabriele, 1956- editor.

Rödder, Wilhelm, editor.

Kulmann, Friedhelm, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 3301.

Lecture Notes in Artificial Intelligence ; 3301

Language:

English

Subjects (All):

Artificial intelligence.

Logic, Symbolic and mathematical.

Artificial Intelligence.

Mathematical Logic and Formal Languages.

Local Subjects:

Artificial Intelligence.

Mathematical Logic and Formal Languages.

Physical Description:

1 online resource (XII, 219 pages).

Edition:

First edition 2005.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005.

System Details:

text file PDF

Summary:

Conditionals are fascinating and versatile objects of knowledge representation. On the one hand, they may express rules in a very general sense, representing, for example, plausible relationships, physical laws, and social norms. On the other hand, as default rules or general implications, they constitute a basic tool for reasoning, even in the presence of uncertainty. In this sense, conditionals are intimately connected both to information and inference. Due to their non-Boolean nature, however, conditionals are not easily dealt with. They are not simply true or false - rather, a conditional "if A then B" provides a context, A, for B to be plausible (or true) and must not be confused with "A entails B" or with the material implication "not A or B." This ill- trates how conditionals represent information, understood in its strict sense as reduction of uncertainty. To learn that, in the context A, the proposition B is plausible, may reduce uncertainty about B and hence is information. The ab- ity to predict such conditioned propositions is knowledge and as such (earlier) acquired information. The ?rst work on conditional objects dates back to Boole in the 19th c- tury, and the interest in conditionals was revived in the second half of the 20th century, when the emerging Arti?cial Intelligence made claims for appropriate formaltoolstohandle"generalizedrules."Sincethen,conditionalshavebeenthe topic of countless publications, each emphasizing their relevance for knowledge representation, plausible reasoning, nonmonotonic inference, and belief revision.

Contents:

Invited Papers

What Is at Stake in the Controversy over Conditionals

Reflections on Logic and Probability in the Context of Conditionals

Acceptance, Conditionals, and Belief Revision

Regular Papers

Getting the Point of Conditionals: An Argumentative Approach to the Psychological Interpretation of Conditional Premises

Projective Default Epistemology

On the Logic of Iterated Non-prioritised Revision

Assertions, Conditionals, and Defaults

A Maple Package for Conditional Event Algebras

Conditional Independences in Gaussian Vectors and Rings of Polynomials

Looking at Probabilistic Conditionals from an Institutional Point of View

There Is a Reason for Everything (Probably): On the Application of Maxent to Induction

Completing Incomplete Bayesian Networks.

Other Format:

Printed edition:

ISBN:

978-3-540-32235-1

9783540322351

Access Restriction:

Restricted for use by site license.