Conditionals, Information, and Inference

1. Front Matter

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2. Invited Papers

   1. What Is at Stake in the Controversy over Conditionals

      • Ernest W. Adams

      Pages 1-11

   2. Reflections on Logic and Probability in the Context of Conditionals

      • Philip G. Calabrese

      Pages 12-37

   3. Acceptance, Conditionals, and Belief Revision

      • Didier Dubois, Hélène Fargier, Henri Prade

      Pages 38-58

3. Regular Papers

   1. Getting the Point of Conditionals: An Argumentative Approach to the Psychological Interpretation of Conditional Premises

      • Jean-François Bonnefon, Denis J. Hilton

      Pages 59-64

   2. Projective Default Epistemology

      • Emil Weydert

      Pages 65-85

   3. On the Logic of Iterated Non-prioritised Revision

      • Richard Booth

      Pages 86-107

   4. Assertions, Conditionals, and Defaults

      • Rainer Osswald

      Pages 108-130

   5. A Maple Package for Conditional Event Algebras

      • Piotr Chrzastowski-Wachtel, Jerzy Tyszkiewicz

      Pages 131-151

   6. Conditional Independences in Gaussian Vectors and Rings of Polynomials

      • František Matúš

      Pages 152-161

   7. Looking at Probabilistic Conditionals from an Institutional Point of View

      • Christoph Beierle, Gabriele Kern-Isberner

      Pages 162-179

   8. There Is a Reason for Everything (Probably): On the Application of Maxent to Induction

      • Jeff B. Paris, Alena Vencovská

      Pages 180-199

   9. Completing Incomplete Bayesian Networks

      • Manfred Schramm, Bertram Fronhöfer

      Pages 200-218

4. Back Matter

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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.

• Bayesian network
• bayesian networks
• belief revision
• cognition
• conditional objects
• conditionals
• conditioned propositions
• inference
• knowledge representation
• logic
• non monotonic logic
• plausible reasoning
• practical reasoning
• probabilistic reasoning

• Dept. of Computer Science, TU Dortmund, Dortmund, Germany

  Gabriele Kern-Isberner

• FernUniversität in Hagen, Fachbereich Wirtschaftswissenschaft, Lehrstuhl für BWL, insb. Operations Research, Hagen, Germany

  Wilhelm Rödder

• FernUniversität in Hagen, Fachbereich Wirtschaftswissenschaft, Lehrstuhl BWL, insb. Operations Research, Hagen, Germany

  Friedhelm Kulmann

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