Abductive reasoning

About: Abductive reasoning is a research topic. Over the lifetime, 1917 publications have been published within this topic receiving 44645 citations. The topic is also known as: abduction & abductive inference.

Facets of abductive reasoning

Abstract: Address correspondence to Ephraim Nissan, School of Computing and Mathematical Sciences, University of Greenwich, Queen Mary Court, Maritime Greenwich Campus, 30 Park Row (Old Royal Naval College), Greenwich, London SE10 9LS, England, U.K. E-mail: E.Nissan@greenwich.ac.uk Cybernetics and Systems: An InternationalJournal, 34: 381 399, 2003 Copyright#Taylor & Francis Inc. ISSN: 0196-9722 print/1087-6553 online DOI: 10.1080/01969720390216203

A Neural Network Approach for First-Order Abductive Inference

Abstract: This paper presents a neural network approach for first-order abductive inference by generalising an existing method from propositional logic to the first-order case. We show how the original propositional method can be extended to enable the grounding of a first-order abductive problem; and we also show how it can be modified to allow the prioritised computation of minimal solutions. We illustrate the approach on a well-known abductive problem and explain how it can be used to perform first-order conditional query answering.

Tabu search approach to fault diagnosis in power systems using fuzzy abductive inference

Abstract: There are two kinds of uncertainties involved in the fault diagnosis problem in power systems, ie, the operating reliabilities of protective relays and circuit breakers, and the correctness of the received and nonreceived alarm signals The accuracy of the fault section estimation is affected to some extent by the method used to deal with the uncertainties encountered in the fault section estimation problem Up to now, a formal and systematic approach to simultaneously account for these two kinds of uncertainties is not available This paper presents a fuzzy abductive inference model and a Tabu search (TS) based approach to the fault diagnosis problem in power systems, which can simultaneously take into account of the above mentioned two kinds of uncertainties At first, the fault diagnosis problem is formulated as a 0 1 integer programming problem, and then the TS approach is presented for solving this problem Test results for a sample power system have shown that the developed fault diagnosis model and method are correct and efficient, and are of promise for on line application in actual power systems

A formal approach to deriving factored evolutionary algorithm architectures

Abstract: Factored Evolutionary Algorithms (FEA) are a class of evolutionary search-based optimization algorithms that have been applied successfully to various problems, such as training neural networks and performing abductive inference in graphical models An FEA is unique in that it factors the objective function by creating overlapping subpopulations that optimize over a subset of variables of the function One consideration in using an FEA is determining the appropriate factor architecture, which determines the set of variables each factor will optimize In this paper, we provide a formal method for deriving factor architectures and give theoretical justification for its use Specifically, we utilize factor graphs of variables in probabilistic graphical models as a way to define factor architectures We also prove how a class of problems, like maximizing NK landscapes, are equivalent to abductive inference in probabilistic graphical models This allows us to take a factor graph architecture and apply it to NK landscapes and a set of commonly used benchmark functions Finally, we show empirically that using the factor graph representation to derive factors for FEA provides the best performance in the majority of cases studied