## Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference (Google eBook)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. |

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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