Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference - Tapa blanda

Goertzel, Ben; Iklé, Matthew; Goertzel, Izabela Freire; Heljakka, Ari

 
9781441945785: Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference
Tapa blanda
ISBN 10: 1441945784 ISBN 13: 9781441945785
Editorial: Springer, 2010

Sinopsis

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 foundational choices we made, introduce PLN knowledge representation, and briefly introduce inference rules and truth-values. We also place PLN in context with other approaches to uncertain inference. 1.1 Motivations This book presents Probabilistic Logic Networks (PLN), a systematic and pragmatic framework for computationally carrying out uncertain reasoning – r- soning about uncertain data, and/or reasoning involving uncertain conclusions. We begin with a few comments about why we believe this is such an interesting and important domain of investigation. First of all, we hold to a philosophical perspective in which “reasoning” – properly understood – plays a central role in cognitive activity. We realize that other perspectives exist; in particular, logical reasoning is sometimes construed as a special kind of cognition that humans carry out only occasionally, as a deviation from their usual (intuitive, emotional, pragmatic, sensorimotor, etc.) modes of thought. However, we consider this alternative view to be valid only according to a very limited definition of “logic.” Construed properly, we suggest, logical reasoning may be understood as the basic framework underlying all forms of cognition, including those conventionally thought of as illogical and irrational.

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De la contraportada

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.

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Editorial
Springer
Año de publicación
2010
Idioma
Inglés
ISBN 10 
1441945784
ISBN 13 
9781441945785
Encuadernación
Tapa blanda
Número de páginas
344
Contacto del fabricante
no disponible
Persona responsable
CMN Printpool Inh. Mathis Neumann
https://www.also.com/ec/cms5/en_6000/6000/index.jsp

Friedrich-Karl-Str. 9
Hamburg
Alemania

Otras ediciones populares con el mismo título

9780387768717: Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference

Edición Destacada

ISBN 10:  0387768718 ISBN 13:  9780387768717
Editorial: Springer, 2008
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