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Probabilistic Logic Networks : A Comprehensive Framework for Uncertain Inference - Ben Goertzel

Probabilistic Logic Networks

A Comprehensive Framework for Uncertain Inference

Hardcover

Published: 11th November 2008
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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.

Introductionp. 1
Knowledge Representationp. 23
Experiential Semanticsp. 41
Indefinite Truth Valuesp. 49
First-Order Extensional Inference: Rules and Strength Formulasp. 63
First-Order Extensional Inference with Indefinite Truth Valuesp. 131
First-Order Extensional Inference with Distributional Truth Valuesp. 141
Error Magnification in Inference Formulasp. 149
Large-Scale Inference Strategiesp. 179
Higher-Order Extensional Inferencep. 201
Handling Crisp and Fuzzy Quantifiers with Indefinite Truth Valuesp. 239
Intensional Inferencep. 249
Aspects of Inference Controlp. 265
Temporal and Causal Inference (Coauthored with Jeff Pressing)p. 279
Comparison of PLN Rules with NARS Rulesp. 307
Referencesp. 321
Indexp. 329
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9780387768717
ISBN-10: 0387768718
Audience: Tertiary; University or College
Format: Hardcover
Language: English
Number Of Pages: 336
Published: 11th November 2008
Publisher: Springer-Verlag New York Inc.
Country of Publication: US
Dimensions (cm): 23.5 x 15.88  x 1.91
Weight (kg): 0.59