Probabilistic Logic Networks

A Comprehensive Framework for Uncertain Inference

By: Ben Goertzel, Matthew Ikle, Izabela Freire Goertzel

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Hardcover | 28 November 2008

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344 Pages

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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.

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Non-Fiction Mathematics Probability & Statistics Computing & I.T.Computer Science Artificial Intelligence Databases Data Capture & Analysis Mathematical Theory of Computation

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Paperback

Published: 12th October 2010

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Introduction	p. 1
Knowledge Representation	p. 23
Experiential Semantics	p. 41
Indefinite Truth Values	p. 49
First-Order Extensional Inference: Rules and Strength Formulas	p. 63
First-Order Extensional Inference with Indefinite Truth Values	p. 131
First-Order Extensional Inference with Distributional Truth Values	p. 141
Error Magnification in Inference Formulas	p. 149
Large-Scale Inference Strategies	p. 179
Higher-Order Extensional Inference	p. 201
Handling Crisp and Fuzzy Quantifiers with Indefinite Truth Values	p. 239
Intensional Inference	p. 249
Aspects of Inference Control	p. 265
Temporal and Causal Inference (Coauthored with Jeff Pressing)	p. 279
Comparison of PLN Rules with NARS Rules	p. 307
References	p. 321
Index	p. 329
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Probabilistic Logic Networks

A Comprehensive Framework for Uncertain Inference

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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