ISBN: 9780387768724
Abstract In this chapter we provide an overview of probabilistic logic networks (PLN), including our motivations for developing PLN and the guiding principles underlying PLN.We discuss fo… Altro …
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2008, ISBN: 9780387768724
A Comprehensive Framework for Uncertain Inference, eBooks, eBook Download (PDF), 2009, [PU: Springer US], Springer US, 2008
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2008, ISBN: 9780387768724
A Comprehensive Framework for Uncertain Inference, eBooks, eBook Download (PDF), 2009, [PU: Springer US], Springer US, 2008
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ISBN: 9780387768724
Abstract In this chapter we provide an overview of probabilistic logic networks (PLN), including our motivations for developing PLN and the guiding principles underlying PLN.We discuss fo… Altro …
Izabela Freire Goertzel/ Ari Heljakka/ Matthew Iklé/ Ben Goertzel:
Probabilistic Logic Networks - nuovo libroISBN: 9780387768724
*Probabilistic Logic Networks* - A Comprehensive Framework for Uncertain Inference / pdf eBook für 149.99 € / Aus dem Bereich: eBooks, Sachthemen & Ratgeber, Computer & Internet Medien > … Altro …
ISBN: 9780387768724
Probabilistic Logic Networks - A Comprehensive Framework for Uncertain Inference: ab 149.99 € eBooks > Sachthemen & Ratgeber > Computer & Internet Springer-Verlag GmbH eBook als pdf, Spri… Altro …
2008, ISBN: 9780387768724
A Comprehensive Framework for Uncertain Inference, eBooks, eBook Download (PDF), 2009, [PU: Springer US], Springer US, 2008
2008, ISBN: 9780387768724
A Comprehensive Framework for Uncertain Inference, eBooks, eBook Download (PDF), 2009, [PU: Springer US], Springer US, 2008
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Informazioni dettagliate del libro - Probabilistic Logic Networks
EAN (ISBN-13): 9780387768724
Anno di pubblicazione: 2008
Editore: Springer-Verlag GmbH
336 Pagine
Lingua: eng/Englisch
Libro nella banca dati dal 2008-02-18T13:38:10+01:00 (Zurich)
Pagina di dettaglio ultima modifica in 2023-10-26T00:22:23+02:00 (Zurich)
ISBN/EAN: 9780387768724
ISBN - Stili di scrittura alternativi:
978-0-387-76872-4
Stili di scrittura alternativi e concetti di ricerca simili:
Autore del libro : freire, goer, goertz, karin, ikle, ari ben menashe
Dati dell'editore
Autore: Ben Goertzel; Matthew Iklé; Izabela Freire Goertzel; Ari Heljakka
Titolo: Probabilistic Logic Networks - A Comprehensive Framework for Uncertain Inference
Editore: Springer; Springer US
336 Pagine
Anno di pubblicazione: 2008-12-16
New York; NY; US
Lingua: Inglese
149,79 € (DE)
154,00 € (AT)
177,00 CHF (CH)
Available
VIII, 336 p.
EA; E107; eBook; Nonbooks, PBS / 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; B; Artificial Intelligence; Mathematical Applications in Computer Science; 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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