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Goertzel, Ben; Heljakka, Ari; Goertzel, Izabela Freire; Iklé, Matthew:
Probabilistic Logic Networks A Comprehensive Framework for Uncertain Inference - Première édition

2008

ISBN: 0387768718

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1st Edition.2nd Printing. 2008 Gebundene Ausgabe Fuzzy Logik - Fuzzy Set, Schluss (logisch), Wahrscheinlichkeit - Wahrscheinlichkeitstheorie, Künstliche Intelligenz, fuzzy; experiential… Plus…

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Probabilistic Logic Networks - edition reliée, livre de poche

2008, ISBN: 9780387768717

A Comprehensive Framework for Uncertain Inference, Buch, Hardcover, 1st Edition. 2nd Printing. 2008, [PU: Springer-Verlag New York Inc.], Springer-Verlag New York Inc., 2008

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Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference - edition reliée, livre de poche

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Probabilistic Logic Networks
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Titre:
ISBN:

0387768718


This book describes Probabilistic Logic Networks (PLN), a novel conceptual, mathematical and computational approach to uncertain inference. In order to carry out effective reasoning in real-world circumstances, AI software must robustly handle uncertainty. However, previous approaches to uncertain inference do not have the breadth of scope required to provide an integrated treatment of the disparate forms of cognitively critical uncertainty as they manifest themselves within the various forms of pragmatic inference. Going beyond prior probabilistic approaches to uncertain inference, PLN is able to encompass within uncertain logic such ideas as induction, abduction, analogy, fuzziness and speculation, and reasoning about time and causality. The book reviews the conceptual and mathematical foundations of PLN, giving the specific algebra involved in each type of inference encompassed within PLN. Inference control and the integration of inference with other cognitive faculties are also briefly discussed.

Informations détaillées sur le livre - Probabilistic Logic Networks


EAN (ISBN-13): 9780387768717
ISBN (ISBN-10): 0387768718
Version reliée
Livre de poche
Date de parution: 2008
Editeur: Springer-Verlag New York Inc.
333 Pages
Poids: 0,590 kg
Langue: eng/Englisch

Livre dans la base de données depuis 2007-11-02T14:05:09+01:00 (Paris)
Page de détail modifiée en dernier sur 2024-04-16T22:10:53+02:00 (Paris)
ISBN/EAN: 0387768718

ISBN - Autres types d'écriture:
0-387-76871-8, 978-0-387-76871-7
Autres types d'écriture et termes associés:
Auteur du livre: ben goertzel, freire, goertz, ikle, ari ben menashe
Titre du livre: probabilistic, uncertain inference, logic, framework, ben

Données de l'éditeur

Auteur: Ben Goertzel; Matthew Iklé; Izabela Freire Goertzel; Ari Heljakka
Titre: Probabilistic Logic Networks - A Comprehensive Framework for Uncertain Inference
Editeur: Springer; Springer US
336 Pages
Date de parution: 2008-11-11
New York; NY; US
Langue: Anglais
160,49 € (DE)
164,99 € (AT)
177,00 CHF (CH)
Available
VIII, 336 p.

BB; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Fuzzy; experiential semantics; first-order extensional inference; higher-order extensional inference; intensional inference; knowledge representation; large-scale inference; logic; probabilistic logic networks; semantics; temporal and causal inference; uncertainty; Artificial Intelligence; Mathematical Applications in Computer Science; Theoretische Informatik; BC

This book describes Probabilistic Logic Networks (PLN), a novel conceptual, mathematical and computational approach to uncertain inference. Going beyond prior probabilistic approaches to uncertain inference, PLN encompasses such ideas as induction, abduction, analogy, fuzziness and speculation, and reasoning about time and causality. The book provides an overview of PLN in the context of other approaches to uncertain inference. Topics addressed in the text include: the basic formalism of PLN knowledge representation the conceptual interpretation of the terms used in PLN an indefinite probability approach to quantifying uncertainty, providing a general method for calculating the "weight-of-evidence" underlying the conclusions of uncertain inference specific PLN inference rules and the corresponding truth-value formulas used to determine the strength of the conclusion of an inference rule from the strengths of the premises large-scale inference strategies inference using variables indefinite probabilities involving quantifiers inheritance based on properties or patterns the Novamente Cognition Engine, an application of PLN temporal and causal logic in PLN Researchers and graduate students in artificial intelligence, computer science, mathematics and cognitive sciences will find this novel perspective on uncertain inference a thought-provoking integration of ideas from a variety of other lines of inquiry.
Provides a comprehensive framework for uncertain reasoning, integrating probability theory, predicate and term logic, and pattern theory Considers a broad scope of reasoning types Fuses rigorous mathematics with practical computation to describe methods designed for large-scale and, in many cases, real-time inference within commercial software systems
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