## Probabilistic Logic Networks: A Comprehensive Framework for Uncertain InferenceAbstract 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. |

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Signale Und Ubertragungssysteme: Modelle Und Verfahren Fur Die ... W. Rupprecht No preview available - 1993 |

### Common terms and phrases

abduction approach assume Atom axes=BOXED backward chaining basic Bayes Bayesian Boolean calculate Chapter cognitive combinatory logic concepts conclusion context Cox’s credible interval deduction formula deduction rule defined denote derived distributional truth-values error estimate Evaluation EvaluationLink Event Calculus example ExOut extensional first-order function fuzzy set given Goertzel graphs heuristic Implication imprecise probabilities indefinite probabilities indefinite truth-values independence assumption induction inference control inference formulas inference rules inference step input instance intABC intensional IntensionalInheritance interpretation interval inversion involving isMale knowledge knowledge representation ListLink logical relationships mathematical means modus ponens multideduction NARS notation observations parameter plausibility PLN deduction PLN inference predicate logic PredictiveImplication premises probabilistic inference probabilistic logic probability distribution probability theory problem quantifiers SatisfyingSet second-order semantics simAB simBC simple strength formulas strength values Stripedog Subset temporal term logic term probability tion truth-value uncertain inference universe variables Walley’s weight of evidence

### References to this book

Artificial General Intelligence, 2008: Proceedings of the First AGI Conference Pei Wang Limited preview - 2008 |