04507nam 2200649 450 991048412580332120220428111521.010.1007/b107184(CKB)1000000000212898(SSID)ssj0000316974(PQKBManifestationID)11237053(PQKBTitleCode)TC0000316974(PQKBWorkID)10287822(PQKB)11229400(DE-He213)978-3-540-32235-1(MiAaPQ)EBC4976816(MiAaPQ)EBC6700585(Au-PeEL)EBL4976816(CaONFJC)MIL140249(OCoLC)1024281024(Au-PeEL)EBL6700585(PPN)123093317(EXLCZ)99100000000021289820220428d2005 uy 0engurnn|008mamaatxtccrConditionals, information, and inference 2002, Hagen, Germany, May 13-15, 2002, revised selected papers /edited by Gabriele Kern-Isberner, Wilhelm Rödder, Friedhelm Kulmann1st ed. 2005.Berlin, Germany ;New York, New York :Springer,[2005]©20051 online resource (XII, 219 p.) Lecture Notes in Artificial Intelligence ;3301Bibliographic Level Mode of Issuance: Monograph3-540-32235-3 3-540-25332-7 Includes bibliographical references and index.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.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.Lecture Notes in Artificial Intelligence ;3301Computational complexityCongressesUncertainty (Information theory)CongressesComputational complexityUncertainty (Information theory)511.352Rödder WilhelmKern-Isberner Gabriele1956-Kulmann FriedhelmMiAaPQMiAaPQMiAaPQBOOK9910484125803321Conditionals, Information, and Inference772476UNINA