Get Abductive Reasoning and Learning PDF

By Peter A. Flach, Antonis C. Kakas (auth.), Dov M. Gabbay, Rudolf Kruse (eds.)

ISBN-10: 9048155606

ISBN-13: 9789048155606

ISBN-10: 9401717338

ISBN-13: 9789401717335

This ebook comprises top survey papers at the a variety of features of Abduction, either logical and numerical ways. Abduction is principal to all parts of utilized reasoning, together with synthetic intelligence, philosophy of technology, laptop studying, info mining and choice thought, in addition to good judgment itself.

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For a GABRIELE PAUL 50 formula w a pair (cp +, cp _) with cp + and cp _ consisting of instances of elements from A, is an explanation of w with respect to the abductive framework, if • (TUcp+)\cp_ FW; • (T u cp +) \ cp _ is consistent. An anti-explanation is defined correspondingly with the first condition being (TU cp+)\cp-lfw. Example: (taken from [Inoue and Sakama, 1995]) Let the theories given by Ti. ,ab(Tweety)}, 0). , 72 = Ti U {---,ab(Tweety)} and we later learn that Tweety has lost its ability to fly, an explanation of ---,flies(Tweety) with respect to the new abductive framework (72, A) is given by If ---,ab(Tweety) is added to ({ ab(Tweety)}, {---,ab(Tweety)}).

Especially in the field of text understanding and plan recognition or planning, where reasoning processes of human agents have to be simulated, such a measure that prefers the most natural hypothesis seems inevitable. , [Eshghi and Kowalski, 19881), • the knowledge-level approach ([Levesque, 1989; Inoue and Sakama, 19951), and GABRIELE PAUL 38 • semantic accounts of abduction ([Boutilier and Becher, 1995; Mayer and Pirri, 1996]). In the following, we will describe each of them. 1 A Set-cover-based Approach In set-cover-based approaches a set of explanations is found by selecting a suitable subset from a given set of hypotheses.

In Proceedings of the Eighth International Symposium on Methodologies for Intelligent Systems. volume 869 of Lecture Notes in Artificial Intelligence. pages 336-345. Springer-Verlag. Berlin. 1994. [Carnap. 1950] Rudolf Carnap. Logical Foundations of Probability. Routledge & Kegan Paul. London. 1950. [Cohen. 1992] William W. Cohen. Alxluctive explanation-based learning: a solution to the multiple inconsistent explanation problem. Machine Learning. 1992. (Console and Saitta. 2000] Luca Console and Lorenza Saitta.

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Abductive Reasoning and Learning by Peter A. Flach, Antonis C. Kakas (auth.), Dov M. Gabbay, Rudolf Kruse (eds.)


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