Probabilistic logic networks : a comprehensive conceptual, mathematical and computational framework for uncertain inference / by Ben Goertzel ... [and others].

Format:

Book

Contributor:

Goertzel, Ben.

Hazel M. Hussong Fund.

Language:

English

Subjects (All):

Probabilities.

Physical Description:

333 pages : illustrations ; 24 cm

Place of Publication:

New York ; London : Springer, 2008.

Summary:

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.

Contents:

2 Knowledge Representation 23

3 Experiential Semantics 41

4 Indefinite Truth Values 49

5 First-Order Extensional Inference: Rules and Strength Formulas / Coauthored with Jeff Pressing 63

6 First-Order Extensional Inference with Indefinite Truth Values 131

7 First-Order Extensional Inference with Distributional Truth Values 141

8 Error Magnification in Inference Formulas 149

9 Large-Scale Inference Strategies 179

10 Higher-Order Extensional Inference 201

11 Handling Crisp and Fuzzy Quantifiers with Indefinite Truth Values 239

12 Intensional Inference 249

13 Aspects of Inference Control 265

14 Temporal and Causal Inference (Coauthored with Jeff Pressing) 279

Appendix A Comparison of PLN Rules with NARS Rules 307.

Notes:

Includes bibliographical references and index.

Local Notes:

Acquired for the Penn Libraries with assistance from the Hazel M. Hussong Fund.

ISBN:

0387768718

9780387768717

OCLC:

182663438