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Goertzel, Ben; Heljakka, Ari; Goertzel, Izabela Freire; Iklé, Matthew:
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Detalles del libro - Probabilistic Logic Networks
EAN (ISBN-13): 9780387768717
ISBN (ISBN-10): 0387768718
Tapa dura
Tapa blanda
Año de publicación: 2008
Editorial: Springer-Verlag New York Inc.
333 Páginas
Peso: 0,590 kg
Idioma: eng/Englisch
Libro en la base de datos desde 2007-11-02T14:05:09+01:00 (Madrid)
Página de detalles modificada por última vez el 2023-10-26T00:22:26+02:00 (Madrid)
ISBN/EAN: 9780387768717
ISBN - escritura alterna:
0-387-76871-8, 978-0-387-76871-7
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: ben goertzel, freire, goertz, ikle, ari ben menashe
Título del libro: probabilistic, uncertain inference, logic, framework, ben
Datos del la editorial
Autor: Ben Goertzel; Matthew Iklé; Izabela Freire Goertzel; Ari Heljakka
Título: Probabilistic Logic Networks - A Comprehensive Framework for Uncertain Inference
Editorial: Springer; Springer US
336 Páginas
Año de publicación: 2008-11-11
New York; NY; US
Idioma: Inglés
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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