Probabilistic Logic Networks

A Comprehensive Framework for Uncertain Inference

  • Ben Goertzel
  • Matthew  Iklé
  • Izabela Freire Goertzel
  • Ari Heljakka

Table of contents

  1. Front Matter
    Pages 1-5
  2. Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka
    Pages 1-21
  3. Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka
    Pages 1-17
  4. Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka
    Pages 1-7
  5. Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka
    Pages 1-14
  6. Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka
    Pages 1-67
  7. Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka
    Pages 1-10
  8. Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka
    Pages 1-7
  9. Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka
    Pages 1-30
  10. Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka
    Pages 1-22
  11. Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka
    Pages 1-37
  12. Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka
    Pages 1-10
  13. Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka
    Pages 1-16
  14. Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka
    Pages 1-13
  15. Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka
    Pages 1-28
  16. Back Matter
    Pages 1-26

About this book

Introduction

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.

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

Authors and affiliations

  • Ben Goertzel
    • 1
  • Matthew  Iklé
    • 2
  • Izabela Freire Goertzel
    • 3
  • Ari Heljakka
    • 4
  1. 1.Novamente LLCRockvilleU.S.A.
  2. 2.Dept. Chemistry, Computer Science &, MathematicsAdams State CollegeAlamosaU.S.A.
  3. 3.Novamente LLCRockvilleU.S.A.
  4. 4.Novamente LLCRockvilleU.S.A.

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-76872-4
  • Copyright Information Springer Science+Business Media, LLC 2009
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-0-387-76871-7
  • Online ISBN 978-0-387-76872-4
  • About this book