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Computer Science - Artificial Intelligence | Probabilistic Logic Networks - A Comprehensive Framework for Uncertain Inference

Probabilistic Logic Networks

A Comprehensive Framework for Uncertain Inference

Goertzel, B., Iklé, M., Goertzel, I.F., Heljakka, A.

1st Edition. 2nd Printing. 2008, VIII, 336p.

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  • Provides a comprehensive framework for uncertain reasoning, integrating probability theory, predicate and term logic, and pattern theory
  • Considers a broad scope of reasoning types
  • Fuses rigorous mathematics with practical computation to describe methods designed for large-scale and, in many cases, real-time inference within commercial software systems

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.

Content Level » Research

Keywords » Fuzzy - experiential semantics - first-order extensional inference - higher-order extensional inference - intensional inference - knowledge representation - large-scale inference - logic - probabilistic logic networks - semantics - temporal and causal inference - uncertainty

Related subjects » Artificial Intelligence - Theoretical Computer Science

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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