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Workshop
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Proceedings of the workshop available at Springer: LNAI 3301. |
| WORKSHOP DESCRIPTION |
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Conditionals, most generally expressed as if-then-statements and also termed default rules, are crucial pieces of information. They represent, for instance, causal or plausible connections, bring isolated facts together and help us obtain a coherent image of the world. Conditional knowledge often is generic knowledge, which has been acquired inductively from experience or learned from authorities. Conditionals tie a flexible and highly interrelated network of links along which reasoning is possible and which can be applied to different situations.
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". First work on conditional objects dates back to Boole in the 19th century, and the interest in conditionals was revived in the second half of the last century, when the emerging Artificial Intelligence claimed for appropriate formal tools for handling "generalized rules". Since then, conditionals have been the topic of countless publications, each emphasizing their relevance for knowledge representation, plausible reasoning, nonmonotonic inference, and belief revision. Moreover, conditionals are closely related to information, understood 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 ability to predict such conditioned propositions is knowledge and as such (earlier) acquired information.
To date, a diversity of default and conditional theories have been brought forth, in quantitative as well as in qualitative frameworks, but clear benchmarks are still in discussion. Therefore, the proper handling of conditionals and information is still a challenge both for theoretical issues and practical applications.
The aim of this workshop is to bring together researchers interested in and working with conditionals and information processing, in order to present new results, discuss open problems and intensify cooperation. Special focuses will be put on the relationship between conditionals and information, on the one hand, and on plausible inference operations as the crucial link between antecedents and conclusions of conditionals, on the other hand.
Areas of interest are:
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| WORKSHOP ORGANIZERS AND PROGRAM CO-CHAIRS |
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Gabriele Kern-Isberner
Fachbereich Informatik
Lehrgebiet Praktische Informatik VIII
FernUniversität Hagen
P.O. Box 940
D-58084 Hagen, Germany
E-Mail: gabriele.kern-isberner@fernuni-hagen.de
Wilhelm Rödder
Fachbereich Wirtschaftswissenschaft
Lehrstuhl BWL, insb. Operations Research
FernUniversität Hagen
P.O. Box 940
D-58084 Hagen, Germany
E-Mail: wilhelm.roedder@fernuni-hagen.de
LOCAL ORGANIZATION
Friedhelm Kulmann
Fachbereich Wirtschaftswissenschaft
Lehrstuhl BWL, insb. Operations Research
FernUniversität Hagen
P.O. Box 940
D-58084 Hagen, Germany
E-Mail: friedhelm.kulmann@fernuni-hagen.de
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| WORKSHOP PROGRAM COMMITTEE |
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Salem Benferhat, Université Paul Sabatier, France
Alexander Bochman, Holon Academic Institute of Technology, Israel
Gerhard Brewka, Universität Leipzig, Germany
Phil Calabrese, SPAWAR/NAVY San Diego, USA
James P. Delgrande, Simon Fraser University, Canada
Didier Dubois, Université Paul Sabatier, France
Angelo Gilio, Università La Sapienza, Italy
Andreas Herzig, Université Paul Sabatier, France
Gabriele Kern-Isberner, FernUniversität in Hagen, Germany
Thomas Lukasiewicz, TU Wien, Austria
Frantisek Matús, Academy of Sciences, Czech Republic
Jeff Paris, University of Manchester, United Kingdom
Simon Parsons, University of Liverpool, United Kingdom
Wilhelm Rödder, FernUniversität in Hagen, Germany
Hans Rott, Universität Regensburg, Germany
Manfred Schramm, TU München, Germany
Milan Studeny, Academy of Sciences, Czech Republic
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Last Modified: 30-May-2005