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

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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Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Kern-Isberner, Gabriele (Επιμελητής έκδοσης), Rödder, Wilhelm (Επιμελητής έκδοσης), Kulmann, Friedhelm (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005.
Σειρά:Lecture Notes in Computer Science, 3301
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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245 1 0 |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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505 0 |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. 
520 |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. 
650 0 |a Computer science. 
650 0 |a Mathematical logic. 
650 0 |a Artificial intelligence. 
650 1 4 |a Computer Science. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Mathematical Logic and Formal Languages. 
700 1 |a Kern-Isberner, Gabriele.  |e editor. 
700 1 |a Rödder, Wilhelm.  |e editor. 
700 1 |a Kulmann, Friedhelm.  |e editor. 
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830 0 |a Lecture Notes in Computer Science,  |x 0302-9743 ;  |v 3301 
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