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.
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.