Probabilistic Logic Networks

A Comprehensive Framework for Uncertain Inference

Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, Ari Heljakka (Autoren)

Buch | Softcover

336 Seiten

2010 | Softcover reprint of hardcover 1st ed. 2009
Springer-Verlag New York Inc.
978-1-4419-4578-5 (ISBN)

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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.

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.

Zusatzinfo	VIII, 336 p.
Verlagsort	New York, NY
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
	Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra
ISBN-10	1-4419-4578-4 / 1441945784
ISBN-13	978-1-4419-4578-5 / 9781441945785
Zustand	Neuware