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[null,"1.","Record Nr.",null,"UNINA9910484125803321"]
[null,null,"Titolo",null,"Conditionals, Information, and Inference : International Workshop, WCII 2002, Hagen, Germany, May 13-15, 2002, Revised Selected Papers / / edited by Gabriele Kern-Isberner, Wilhelm Rödder, Friedhelm Kulmann"]
[null,null,"Pubbl/distr/stampa",null,"Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2005"]
[null,null,"Edizione",null,"[1st ed. 2005.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XII, 219 p.)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 3301"]
[null,null,"Disciplina",null,"511.352"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Machine theory"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Formal Languages and Automata Theory"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Invited Papers -- What Is at Stake in the Controversy over Conditionals -- Reflections on Logic and Probability in the Context of Conditionals -- Acceptance, Conditionals, and Belief Revision -- Regular Papers -- Getting the Point of Conditionals: An Argumentative Approach to the Psychological Interpretation of Conditional Premises -- Projective Default Epistemology -- On the Logic of Iterated Non-prioritised Revision -- Assertions, Conditionals, and Defaults -- A Maple Package for Conditional Event Algebras -- Conditional Independences in Gaussian Vectors and Rings of Polynomials -- Looking at Probabilistic Conditionals from an Institutional Point of View -- There Is a Reason for Everything (Probably): On the Application of Maxent to Induction -- Completing Incomplete Bayesian Networks."]
[null,null,null,"Sommario/riassunto","Conditionals are fascinating and versatile objects of knowledge representation. On the one hand, they may express rules in a very general sense, representing, for example, plausible relationships, physical laws, and social norms. On the other hand, as default rules or general implications, they constitute a basic tool for reasoning, even in the presence of uncertainty. In this sense, conditionals are intimately connected both to information and inference. Due to their non-Boolean nature, however, conditionals are not easily dealt with. They are not simply true or false — rather, a conditional “if A then B” provides a "]
[{"$nulls":4},"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."]