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

Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink Bücher | Lecture notes in computer science ; 3301Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005Description: Online-Ressource (XII, 219 p. Also available online, digital)ISBN:
  • 9783540322351
Subject(s): Genre/Form: Additional physical formats: 9783540253327 | Buchausg. u.d.T.: Conditionals, information, and inference. Berlin : Springer, 2005. XII, 218 SMSC: MSC: *68-06 | 68T30 | 68T37 | 00B25RVK: RVK: SS 4800LOC classification:
  • Q334-342 TJ210.2-211.495
DOI: DOI: 10.1007/b107184Online resources: Summary: 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.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.PPN: PPN: 1647641705Package identifier: Produktsigel: ZDB-2-SCS | ZDB-2-LNC | ZDB-2-SEB | ZDB-2-SCS | ZDB-2-SXCS | ZDB-2-SEB
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