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2008, ISBN: 0387768718
[EAN: 9780387768717], Neubuch, [PU: Springer US], FUZZY LOGIK - SET SCHLUSS (LOGISCH) WAHRSCHEINLICHKEIT WAHRSCHEINLICHKEITSTHEORIE EXPERIENTIALSEMANTICS FIRST-ORDEREXTENSIONALINFERENCE H… Mehr…
Goertzel, Ben; Heljakka, Ari; Goertzel, Izabela Freire; Iklé, Matthew:
Probabilistic Logic Networks A Comprehensive Framework for Uncertain Inference - Erstausgabe2008, ISBN: 0387768718
Gebundene Ausgabe
1st Edition.2nd Printing. 2008 Gebundene Ausgabe Fuzzy Logik - Fuzzy Set, Schluss (logisch), Wahrscheinlichkeit - Wahrscheinlichkeitstheorie, Künstliche Intelligenz, fuzzy; experiential… Mehr…
2008
ISBN: 0387768718
Gebundene Ausgabe
1st Edition.2nd Printing. 2008 Gebundene Ausgabe Fuzzy Logik - Fuzzy Set, Schluss (logisch), Wahrscheinlichkeit - Wahrscheinlichkeitstheorie, Künstliche Intelligenz, fuzzy; experiential… Mehr…
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 - gebunden oder broschiert
2008, ISBN: 0387768718
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Detailangaben zum Buch - Probabilistic Logic Networks
EAN (ISBN-13): 9780387768717
ISBN (ISBN-10): 0387768718
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2008
Herausgeber: Springer-Verlag New York Inc.
333 Seiten
Gewicht: 0,590 kg
Sprache: eng/Englisch
Buch in der Datenbank seit 2007-11-02T14:05:09+01:00 (Berlin)
Detailseite zuletzt geändert am 2024-04-16T22:10:53+02:00 (Berlin)
ISBN/EAN: 0387768718
ISBN - alternative Schreibweisen:
0-387-76871-8, 978-0-387-76871-7
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: ben goertzel, freire, goertz, ikle, ari ben menashe
Titel des Buches: probabilistic, uncertain inference, logic, framework, ben
Daten vom Verlag:
Autor/in: Ben Goertzel; Matthew Iklé; Izabela Freire Goertzel; Ari Heljakka
Titel: Probabilistic Logic Networks - A Comprehensive Framework for Uncertain Inference
Verlag: Springer; Springer US
336 Seiten
Erscheinungsjahr: 2008-11-11
New York; NY; US
Sprache: Englisch
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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