Conditionals, Information, and Inference International Workshop, WCII 2002, Hagen, Germany, May 13-15, 2002, Revised Selected Papers

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.

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 cons...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Kern-Isberner, Gabriele (Editor), Rödder, Wilhelm (Editor), Kulmann, Friedhelm (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005.
Edition:1st ed. 2005.
Series:Lecture Notes in Artificial Intelligence ; 3301
Springer eBook Collection.
Subjects:
Artificial intelligence.
Mathematical logic.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of 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.

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