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

Gabriele Kern-Isberner

ISBN 10: 3540253327 / ISBN 13: 9783540253327

Edité par Springer, Springer, 2005

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9783540253327

Détails bibliographiques

• Titre: Conditionals, Information, and Inference : International Workshop, WCII 2002, Hagen, Germany, May 13-15, 2002, Revised Selected Papers
• Éditeur: Springer, Springer
• Date de parution: 2005
• Langue: anglais
• ISBN à 10 chiffres: 3540253327
• ISBN à 13 chiffres: 9783540253327
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 235 L x 155 H x 13 P (en millimètres) 235 millimètres de largeur par 155 millimètres de hauteur par 13 millimètres de profondeur
• Poids: 365 grammes

A propos de ce titre

Synopsis

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.