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Abductive Inference Models for Diagnostic Problem-Solving

TLDR
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.
Abstract
Contents: Abduction and Diagnostic Inference.- Computational Models for Diagnostic Problem Solving.- Basics of Parsimonious Covering Theory.- Probabilistic Causal Model.- Diagnostic Strategies in the Probabilistic Causal Model.- Causal Chaining.- Parallel Processing for Diagnostic Problem-Solving.- Conclusion.- Bibliography.- Index.

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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.
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The complexity of logic-based abduction

Thomas Eiter, +1 more
- 03 Jan 1995 - 
TL;DR: It is shown that with the most basic forms of abduction the relevant decision problems are complete for complexity classes at the second level of the polynomial hierarchy, while the use of prioritization raises the complexity to the third level in certain cases.
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Complex Problem Solving : The European Perspective

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Predictive and diagnostic learning within causal models: Asymmetries in cue competition.

TL;DR: This article showed that diagnostic and predictive reasoning are not even symmetrical, and that cue competition occurs among multiple possible causes during predictive learning, while multiple possible effects need not compete during diagnostic learning.