LEADER 04505nam 2200649 450 001 996465686003316 005 20220428111521.0 024 7 $a10.1007/b107184 035 $a(CKB)1000000000212898 035 $a(SSID)ssj0000316974 035 $a(PQKBManifestationID)11237053 035 $a(PQKBTitleCode)TC0000316974 035 $a(PQKBWorkID)10287822 035 $a(PQKB)11229400 035 $a(DE-He213)978-3-540-32235-1 035 $a(MiAaPQ)EBC4976816 035 $a(MiAaPQ)EBC6700585 035 $a(Au-PeEL)EBL4976816 035 $a(CaONFJC)MIL140249 035 $a(OCoLC)1024281024 035 $a(Au-PeEL)EBL6700585 035 $a(PPN)123093317 035 $a(EXLCZ)991000000000212898 100 $a20220428d2005 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 00$aConditionals, information, and inference $e2002, Hagen, Germany, May 13-15, 2002, revised selected papers /$fedited by Gabriele Kern-Isberner, Wilhelm Ro?dder, Friedhelm Kulmann 205 $a1st ed. 2005. 210 1$aBerlin, Germany ;$aNew York, New York :$cSpringer,$d[2005] 210 4$dİ2005 215 $a1 online resource (XII, 219 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v3301 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-32235-3 311 $a3-540-25332-7 320 $aIncludes bibliographical references and index. 327 $aInvited 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. 330 $aConditionals are fascinating and versatile objects of knowledge representation. On the one hand, they may express rules in a very general sense, representing, for example, plausible relationships, physical laws, and social norms. On the other hand, as default rules or general implications, they constitute a basic tool for reasoning, even in the presence of uncertainty. In this sense, conditionals are intimately connected both to information and inference. Due to their non-Boolean nature, however, conditionals are not easily dealt with. They are not simply true or false ? rather, a conditional ?if A then B? provides a context, A, for B to be plausible (or true) and must not be confused with ?A entails B? or with the material implication ?not A or B.? This ill- trates how conditionals represent information, understood in its strict sense as reduction of uncertainty. To learn that, in the context A, the proposition B is plausible, may reduce uncertainty about B and hence is information. The ab- ity to predict such conditioned propositions is knowledge and as such (earlier) acquired information. The ?rst work on conditional objects dates back to Boole in the 19th c- tury, and the interest in conditionals was revived in the second half of the 20th century, when the emerging Arti?cial Intelligence made claims for appropriate formaltoolstohandle?generalizedrules.?Sincethen,conditionalshavebeenthe topic of countless publications, each emphasizing their relevance for knowledge representation, plausible reasoning, nonmonotonic inference, and belief revision. 410 0$aLecture Notes in Artificial Intelligence ;$v3301 606 $aComputational complexity$vCongresses 606 $aUncertainty (Information theory)$vCongresses 615 0$aComputational complexity 615 0$aUncertainty (Information theory) 676 $a511.352 702 $aRo?dder$b Wilhelm 702 $aKern-Isberner$b Gabriele$f1956- 702 $aKulmann$b Friedhelm 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465686003316 996 $aConditionals, Information, and Inference$9772476 997 $aUNISA