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• Record Nr.: UNISALENTO991002511079707536
• Pubbl/distr/stampa: Milano : Bompiani, 1987
• Disciplina: 803.51
• Soggetti: Letteratura - Enciclopedie e dizionari
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

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

• Record Nr.: UNINA9910484125803321
• Pubbl/distr/stampa: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2005
• Edizione: [1st ed. 2005.]
• Descrizione fisica: 1 online resource (XII, 219 p.)
• Collana: Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 3301
• Disciplina: 511.352
• Soggetti: Artificial intelligence; Machine theory; Artificial Intelligence; Formal Languages and Automata Theory
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• 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 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.