| Conditionals, Information, and Inference: International Workshop, Wcii 2002, Hagen, Germany, May 13-15, 2002, Revised Selected Papers 2005 Edition Contributor(s): Kern-Isberner, Gabriele (Editor), Rödder, Wilhelm (Editor), Kulmann, Friedhelm (Editor) |
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ISBN: 3540253327 ISBN-13: 9783540253327 Publisher: Springer OUR PRICE: $52.24 Product Type: Paperback - Other Formats Published: May 2005 Annotation: This book constitutes the thoroughly refereed postproceedings of the International Workshop on Conditionals, Information, and Inference, WCII 2002, held in Hagen, Germany in May 2002. The 9 revised full papers presented together with 3 invited papers by leading researchers in the area were carefully selected during iterated rounds of reviewing and improvement. The papers address all current issues of research on conditionals, ranging from foundational, theoretical, and methodological aspects to applications in various contexts of knowledge representation. |
| Additional Information |
| BISAC Categories: - Computers | Intelligence (ai) & Semantics - Mathematics | Logic - Computers | Computer Science |
| Dewey: 511.352 |
| LCCN: 2005925863 |
| Series: Lecture Notes in Computer Science |
| Physical Information: 0.5" H x 6.14" W x 9.21" (0.74 lbs) 219 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |