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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.


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01 Jan 2005
TL;DR: An overview of Peirce’s influence seen from the perspective of computer science is recommended as an introduction to the philosopher C.S. Peirces’ work.
Abstract: The philosopher C.S.Peirce (1839–1914) is considered a pioneer in the understanding of human reasoning, especially in the specific context of scientific discovery. His work is often cited in computer science literature but probably only few computer scientists have read Peirce’s original work. We recommend [7] as overview of Peirce’s influence seen from the perspective of computer science. Peirce postulated three principles as the fundamental ones:

1 citations

Patent
01 Oct 2020
TL;DR: In this paper, an autonomous vehicle, system and method of operating the autonomous vehicle are provided, which includes a sensor, a reasoning engine and a navigation system, where the sensor receives token data and the reasoning engine performs an abductive inference on a fact determined from the token data to estimate a backward condition and a deductive inference to the estimated backward condition in to predict a forward condition.
Abstract: An autonomous vehicle, system and method of operating the autonomous vehicle are provided. The system includes a sensor, a reasoning engine and a navigation system. The sensor receives token data. Thereasoning engine performs an abductive inference on a fact determined from the token data to estimate a backward condition, and a deductive inference to the estimated backward condition in to order to predict a forward condition. The navigation system operates the autonomous vehicle based on the predicted forward condition.

1 citations

Posted Content
TL;DR: In this paper, a pure model-theoretic analysis of the possible ways to restore the consistency of distributed data is presented by characterizing the possibilities to recover consistent data from an inconsistent database in terms of those models of the database that exhibit as minimal inconsistent information as reasonably possible.
Abstract: In this paper we consider two points of views to the problem of coherent integration of distributed data. First we give a pure model-theoretic analysis of the possible ways to `repair' a database. We do so by characterizing the possibilities to `recover' consistent data from an inconsistent database in terms of those models of the database that exhibit as minimal inconsistent information as reasonably possible. Then we introduce an abductive application to restore the consistency of a given database. This application is based on an abductive solver (A-system) that implements an SLDNFA-resolution procedure, and computes a list of data-facts that should be inserted to the database or retracted from it in order to keep the database consistent. The two approaches for coherent data integration are related by soundness and completeness results.

1 citations

Posted Content
TL;DR: This work defines a measure of plausibility, with which one can compare different possible explanations, and which can be combined when there are different sets of data, and compares with the conventional measure for probabilities as well as to the proposed measure of possibilities.
Abstract: In the interpretation of experimental data, one is actually looking for plausible explanations. We look for a measure of plausibility, with which we can compare different possible explanations, and which can be combined when there are different sets of data. This is contrasted to the conventional measure for probabilities as well as to the proposed measure of possibilities. We define what characteristics this measure of plausibility should have. In getting to the conception of this measure, we explore the relation of plausibility to abductive reasoning, and to Bayesian probabilities. We also compare with the Dempster-Schaefer theory of evidence, which also has its own definition for plausibility. Abduction can be associated with biconditionality in inference rules, and this provides a platform to relate to the Collins-Michalski theory of plausibility. Finally, using a formalism for wiring logic onto Hopfield neural networks, we ask if this is relevant in obtaining this measure.

1 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022103
202156
202059
201956
201867