| Description |
1 online resource (xii, 218 pages) : illustrations |
| Series |
Lecture notes in computer science, 0302-9743 ; 3301. Lecture notes in artificial intelligence |
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Lecture notes in computer science ; 3301.
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Lecture notes in computer science. Lecture notes in artificial intelligence
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| Contents |
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 |
| Summary |
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 |
| Analysis |
wiskunde |
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mathematics |
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computerwetenschappen |
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computer sciences |
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kunstmatige intelligentie |
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artificial intelligence |
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logica |
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logic |
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Information and Communication Technology (General) |
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Informatie- en communicatietechnologie (algemeen) |
| Bibliography |
Includes bibliographical references and index |
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Includes bibliographical references and author index |
| Notes |
English |
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Print version record |
| Subject |
Knowledge representation (Information theory) -- Congresses
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Conditionals (Logic) -- Congresses
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Inference -- Congresses
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Uncertainty (Information theory) -- Congresses
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Computational complexity -- Congresses
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Uncertainty (Information theory)
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Computational complexity.
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Informatique.
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Computational complexity
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Conditionals (Logic)
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Inference
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Knowledge representation (Information theory)
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Uncertainty (Information theory)
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| Genre/Form |
proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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| Form |
Electronic book
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| Author |
Kern-Isberner, Gabriele, 1956-
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Rödder, Wilhelm.
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Kulmann, Friedhelm.
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| ISBN |
9783540322351 |
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3540322353 |
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3540253327 |
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9783540253327 |
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9788354032236 |
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835403223X |
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