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  • 1
    UID:
    b3kat_BV022301214
    Format: 1 Online-Ressource (XII, 218 S.) , graph. Darst.
    ISBN: 3540253327
    Series Statement: Lecture notes in computer science 3301 : Lectrue notes in artifical intelligence
    Language: English
    Keywords: Wissensverarbeitung ; Ausdruck ; Konditionalsatz ; Mathematische Logik ; Wissensverarbeitung ; Ausdruck ; Konditionalsatz ; Inferenz ; Konferenzschrift
    URL: Volltext  (lizenzpflichtig)
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    b3kat_BV019895078
    Format: XII, 218 S. , graph. Darst.
    ISBN: 3540253327
    Series Statement: Lecture notes in computer science 3301 : Lectrue notes in artifical intelligence
    Language: English
    Keywords: Wissensverarbeitung ; Ausdruck ; Konditionalsatz ; Mathematische Logik ; Wissensverarbeitung ; Ausdruck ; Konditionalsatz ; Inferenz ; Konferenzschrift ; Konferenzschrift ; Konferenzschrift
    URL: Cover
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  • 3
    UID:
    almahu_9947364071402882
    Format: XII, 219 p. , online resource.
    ISBN: 9783540322351
    Series Statement: Lecture Notes in Computer Science, 3301
    Content: 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.
    Note: 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.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783540253327
    Language: English
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  • 4
    UID:
    almahu_9949972517302882
    Format: XII, 219 p. , online resource.
    Edition: 1st ed. 2005.
    ISBN: 9783540322351
    Series Statement: Lecture Notes in Artificial Intelligence, 3301
    Content: 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.
    Note: 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.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783540253327
    Additional Edition: Printed edition: ISBN 9783540809098
    Language: English
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  • 5
    UID:
    kobvindex_ZLB13913501
    Format: XII, 218 Seiten , graph. Darst. , 24 cm
    ISBN: 9783540253327 , 3540253327
    Series Statement: Lecture notes in computer science
    Note: Text engl.
    Language: English
    Keywords: Wissensverarbeitung ; Ausdruck 〈Logik〉 ; Konditionalsatz ; Mathematische Logik ; Kongress ; Hagen 〈2002〉 ; Wissensverarbeitung ; Ausdruck 〈Logik〉 ; Konditionalsatz ; Inferenz 〈Künstliche Intelligenz〉 ; Kongress ; Hagen 〈2002〉 ; Kongress ; Konferenzschrift
    Author information: Kern-Isberner, Gabriele
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