Probabilistic Logic Networks

1. Front Matter

   Pages 1-5

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2. Introduction

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

   Pages 1-21

3. Knowledge Representation

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

   Pages 1-17

4. Experiential Semantics

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

   Pages 1-7

5. Indefinite Truth Values

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

   Pages 1-14

6. First-Order Extensional Inference: Rules and Strength Formulas

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

   Pages 1-67

7. First-Order Extensional Inference with Indefinite Truth Values

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

   Pages 1-10

8. First-Order Extensional Inference with Distributional Truth Values

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

   Pages 1-7

9. Error Magnification in Inference Formulas

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

   Pages 1-30

10. Large-Scale Inference Strategies

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

    Pages 1-22

11. Higher-Order Extensional Inference

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

    Pages 1-37

12. Handling Crisp and Fuzzy Quantifiers with Indefinite Truth Values

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

    Pages 1-10

13. Intensional Inference

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

    Pages 1-16

14. Aspects of Inference Control

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

    Pages 1-13

15. Temporal and Causal Inference

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

    Pages 1-28

16. Back Matter

    Pages 1-26

    PDF

Abstract In this chapter we provide an overview of probabilistic logic networks (PLN), including our motivations for developing PLN and the guiding principles underlying PLN. We discuss foundational choices we made, introduce PLN knowledge representation, and briefly introduce inference rules and truth-values. We also place PLN in context with other approaches to uncertain inference. 1.1 Motivations This book presents Probabilistic Logic Networks (PLN), a systematic and pragmatic framework for computationally carrying out uncertain reasoning – r- soning about uncertain data, and/or reasoning involving uncertain conclusions. We begin with a few comments about why we believe this is such an interesting and important domain of investigation. First of all, we hold to a philosophical perspective in which “reasoning” – properly understood – plays a central role in cognitive activity. We realize that other perspectives exist; in particular, logical reasoning is sometimes construed as a special kind of cognition that humans carry out only occasionally, as a deviation from their usual (intuitive, emotional, pragmatic, sensorimotor, etc.) modes of thought. However, we consider this alternative view to be valid only according to a very limited definition of “logic.” Construed properly, we suggest, logical reasoning may be understood as the basic framework underlying all forms of cognition, including those conventionally thought of as illogical and irrational.

• 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

• Novamente LLC, Rockville, U.S.A.

  Ben Goertzel, Izabela Freire Goertzel, Ari Heljakka

• Dept. Chemistry, Computer Science &, Mathematics, Adams State College, Alamosa, U.S.A.

  Matthew Iklé

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