Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference / Edition 1

Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference / Edition 1

by Ben Goertzel, Matthew Ikle, Izabela Freire Goertzel, Ari Heljakka
     
 

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ISBN-10: 0387768718

ISBN-13: 9780387768717

Pub. Date: 09/28/2008

Publisher: Springer US

This book describes Probabilistic Logic Networks (PLN), a novel conceptual, mathematical and computational approach to uncertain inference. In order to carry out effective reasoning in real-world circumstances, AI software must robustly handle uncertainty. However, previous approaches to uncertain inference do not have the breadth of scope required to provide an

Overview

This book describes Probabilistic Logic Networks (PLN), a novel conceptual, mathematical and computational approach to uncertain inference. In order to carry out effective reasoning in real-world circumstances, AI software must robustly handle uncertainty. However, previous approaches to uncertain inference do not have the breadth of scope required to provide an integrated treatment of the disparate forms of cognitively critical uncertainty as they manifest themselves within the various forms of pragmatic inference. Going beyond prior probabilistic approaches to uncertain inference, PLN is able to encompass within uncertain logic such ideas as induction, abduction, analogy, fuzziness and speculation, and reasoning about time and causality. The book reviews the conceptual and mathematical foundations of PLN, giving the specific algebra involved in each type of inference encompassed within PLN. Inference control and the integration of inference with other cognitive faculties are also briefly discussed.

Product Details

ISBN-13:
9780387768717
Publisher:
Springer US
Publication date:
09/28/2008
Edition description:
1st Edition. 2nd Printing. 2008
Pages:
336
Product dimensions:
6.30(w) x 9.30(h) x 0.80(d)

Table of Contents

Knowledge Representation.- Experiential Semantics.- Indefinite Truth Values.- First-Order Extensional Inference: Rules and Strength Formulas.- First-Order Extensional Inference with Indefinite Truth Values.- First-Order Extensional Inference with Distributional Truth Values.- Error Magnification in Inference Formulas.- Large-Scale Inference Strategies.- Higher-Order Extensional Inference.- Handling Crisp and Fuzzy Quantifiers with Indefinite Truth Values.- Intensional Inference.- Aspects of Inference Control.- Temporal and Causal Inference.

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