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Nonmonotonic Logics [electronic resource] :Basic Concepts, Results, and Techniques /

Contributor(s): Schlechta, Karl [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence: 1187Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1997.Description: IX, 246 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540497363.Subject(s): Computer science | Mathematical logic | Artificial intelligence | Computer Science | Artificial Intelligence (incl. Robotics) | Mathematical Logic and Formal LanguagesOnline resources: Click here to access online
Contents:
Preferential structures and related logics -- Defaults as generalized quantifiers -- Logic and analysis -- Theory revision and probability -- Structured reasoning.
In: Springer eBooksSummary: Nonmonotonic logics were created as an abstraction of some types of common sense reasoning, analogous to the way classical logic serves to formalize ideal reasoning about mathematical objects. These logics are nonmonotonic in the sense that enlarging the set of axioms does not necessarily imply an enlargement of the set of formulas deducible from these axioms. Such situations arise naturally, for example, in the use of information of different degrees of reliability. This book emphasizes basic concepts by outlining connections between different formalisms of nonmonotonic logic, and gives a coherent presentation of recent research results and reasoning techniques. It provides a self-contained state-of-the-art survey of the area addressing researchers in AI lo.
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Preferential structures and related logics -- Defaults as generalized quantifiers -- Logic and analysis -- Theory revision and probability -- Structured reasoning.

Nonmonotonic logics were created as an abstraction of some types of common sense reasoning, analogous to the way classical logic serves to formalize ideal reasoning about mathematical objects. These logics are nonmonotonic in the sense that enlarging the set of axioms does not necessarily imply an enlargement of the set of formulas deducible from these axioms. Such situations arise naturally, for example, in the use of information of different degrees of reliability. This book emphasizes basic concepts by outlining connections between different formalisms of nonmonotonic logic, and gives a coherent presentation of recent research results and reasoning techniques. It provides a self-contained state-of-the-art survey of the area addressing researchers in AI lo.

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