Bayesian nets are widely used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. But many philosophers have criticised and ultimately rejected the central assumption on which such work is based - the Causal Markov Condition. So should Bayesian nets be abandoned? What explains their success in artificial intelligence?
This book argues that the Causal Markov Condition holds as a default rule: it often holds but may need to be repealed in the face of counterexamples. Thus Bayesian nets are the right tool to use by default but naively applying them can lead to problems. The book develops a systematic account of causal reasoning and shows how Bayesian nets can be coherently employed to automate the reasoning processes of an artificial agent.
The resulting framework for causal reasoning involves not only new algorithms but also new conceptual foundations. Probability and causality are treated as mental notions - part of an agent's belief state. Yet probability and causality are also objective - different agents with the same background knowledge ought to adopt the same or similar probabilistic and causal beliefs. This book, aimed at researchers and graduate students in computer science, mathematics and philosophy, provides a general introduction to these philosophical views as well as an exposition of the computational techniques that they motivate.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Jon Williamson is at the Department of Philosophy, Logic, and Scientific Method, London School of Economics, London
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Vendeur : Oxfam Bookshop Gent, Gent, Belgique
Hardcover. Etat : Good. Etat de la jaquette : Poor. 1st Edition. ix - 239 pp. Oxford University Press, Oxford 2005. First Edition. Hardcover. Jacket shows shelve wear and stains. Blue linen covers with gilt spine lettering. Some stains no the fore edge. Some underlining. Ex. library of the Library of the Ghent University: usual labels, stamps and numbers. Otherwise a good copy. N° de réf. du vendeur 011907
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Vendeur : Fireside Bookshop, Stroud, GLOS, Royaume-Uni
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Etat : Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Re-bound by library. Library sticker on front cover. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,650grams, ISBN:019853079X. N° de réf. du vendeur 3716022
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Hardcover. Etat : new. Hardcover. Bayesian nets are widely used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. But many philosophers have criticised and ultimately rejected the central assumption on which such work is based - the Causal Markov Condition. So should Bayesian nets be abandoned? What explains their success in artificialintelligence?This book argues that the Causal Markov Condition holds as a default rule: it often holds but may need to be repealed in the face of counterexamples. Thus Bayesian nets are the right tool to use bydefault but naively applying them can lead to problems. The book develops a systematic account of causal reasoning and shows how Bayesian nets can be coherently employed to automate the reasoning processes of an artificial agent.The resulting framework for causal reasoning involves not only new algorithms but also new conceptual foundations. Probability and causality are treated as mental notions - part of an agent's belief state. Yet probability and causality are alsoobjective - different agents with the same background knowledge ought to adopt the same or similar probabilistic and causal beliefs. This book, aimed at researchers and graduate students in computerscience, mathematics and philosophy, provides a general introduction to these philosophical views as well as an exposition of the computational techniques that they motivate. Bayesian nets are used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. This book brings together how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9780198530794
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Vendeur : Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlande
Etat : New. Bayesian nets are used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. This book brings together how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy. Num Pages: 252 pages, numerous figures. BIC Classification: HPL; PBT; UYQ. Category: (P) Professional & Vocational. Dimension: 240 x 160 x 21. Weight in Grams: 516. . 2004. Hardback. . . . . N° de réf. du vendeur V9780198530794
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Vendeur : Brook Bookstore On Demand, Napoli, NA, Italie
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Vendeur : Kennys Bookstore, Olney, MD, Etats-Unis
Etat : New. Bayesian nets are used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. This book brings together how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy. Num Pages: 252 pages, numerous figures. BIC Classification: HPL; PBT; UYQ. Category: (P) Professional & Vocational. Dimension: 240 x 160 x 21. Weight in Grams: 516. . 2004. Hardback. . . . . Books ship from the US and Ireland. N° de réf. du vendeur V9780198530794
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