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Probabilistic Logic Networks

A Comprehensive Framework for Uncertain Inference

By Ben Goertzel , Matthew Iklé , Izabela Freire Goertzel , Ari Heljakka

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This comprehensive book describes Probabilistic Logic Networks (PLN), a novel conceptual, mathematical and computational approach to uncertain inference. A broad scope of reasoning types are considered.

Full Description

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  • ISBN13: 978-0-3877-6871-7
  • 344 Pages
  • User Level: Science
  • Publication Date: December 16, 2008
  • Available eBook Formats: PDF
Full Description
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.
Table of Contents

Table of Contents

  1. Introduction.
  2. Knowledge Representation.
  3. Experiential Semantics.
  4. Indefinite Truth Values.
  5. First
  6. Order Extensional Inference: Rules and Strength Formulas.
  7. First
  8. Order Extensional Inference with Indefinite Truth Values.
  9. First
  10. Order Extensional Inference with Distributional Truth Values.
  11. Error Magnification in Inference Formulas.
  12. Large
  13. Scale Inference Strategies.
  14. Higher
  15. Order Extensional Inference.
  16. Handling Crisp and Fuzzy Quantifiers with Indefinite Truth Values.
  17. Intensional Inference.
  18. Aspects of Inference Control.
  19. Temporal and Causal Inference.
  20. Appendix A: Comparison of PLN Rules with NARS Rules.
  21. References.
  22. Index.

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