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|a 9783540322351
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|a 10.1007/b107184
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|a Conditionals, Information, and Inference
|h [electronic resource] :
|b International Workshop, WCII 2002, Hagen, Germany, May 13-15, 2002, Revised Selected Papers /
|c edited by Gabriele Kern-Isberner, Wilhelm Rödder, Friedhelm Kulmann.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2005.
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|a XII, 219 p.
|b online resource.
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|a text
|b txt
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|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 3301
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|a 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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|a 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.
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|a Computer science.
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|a Mathematical logic.
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|a Artificial intelligence.
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|a Computer Science.
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|a Artificial Intelligence (incl. Robotics).
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|a Mathematical Logic and Formal Languages.
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|a Kern-Isberner, Gabriele.
|e editor.
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|a Rödder, Wilhelm.
|e editor.
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|a Kulmann, Friedhelm.
|e editor.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783540253327
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|a Lecture Notes in Computer Science,
|x 0302-9743 ;
|v 3301
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|u http://dx.doi.org/10.1007/b107184
|z Full Text via HEAL-Link
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|a ZDB-2-SCS
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|a ZDB-2-LNC
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|a Computer Science (Springer-11645)
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