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Diagnostic Problem-Solving Using Fuzzy Abductive Inference with Degress of Manifestations

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This article is published in Journal of Japan Society for Fuzzy Theory and Systems.The article was published on 1997-01-01 and is currently open access. It has received 1 citations till now. The article focuses on the topics: Abductive reasoning & Fuzzy logic.

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Journal ArticleDOI

A logical framework for default reasoning

TL;DR: A simple logical framework for default reasoning by treating defaults as predefined possible hypotheses is presented, and it is shown how this idea subsumes the intuition behind Reiter's default logic.
Journal ArticleDOI

Probabilistic Horn abduction and Bayesian networks

TL;DR: It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework, and it is argued that it is better to invent new hypotheses to explain dependence rather than having to worry about dependence in the language.
Book

Abductive Inference Models for Diagnostic Problem-Solving

TL;DR: This paper presents a meta-model for parallel processing for Diagnostic Problem-Solving using the probabilistic Causal Model and a parallel processing model based on the Parsimonious Covering Theory.
Journal ArticleDOI

Pictorial representations of fuzzy connectives, part I: cases of t-norms, t-conorms and averaging operators

TL;DR: Part I of this paper shows a number of examples of existing t-norms and t-conorms which are known as good models of fuzzy set-theoretic intersection and union, and then the pictorial representations are made with the aid of a computer.
Proceedings Article

Probabilistic semantics for cost based abduction

TL;DR: This work examines existing methods for constructing explanations (proofs), as a minimization problem on a DAG, and defines a probabilistic semantics for the costs, and proves the equivalence of the cost minimizationproblem to the Bayesian network MAP solution of the system.
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