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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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Journal ArticleDOI
TL;DR: Three types of applicability of abductive reasoning for design synthesis including: identification of implicit design targets, ideation of innovative design concepts, and diagnosis of violating design constraints or design axioms are elaborated.

34 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a notion of "analyzing in the present" as a source of inspiration in analyzing qualitative research materials, which emerged from extensive listening to interview recordings.
Abstract: The article presents a notion of “analyzing in the present” as a source of inspiration in analyzing qualitative research materials. The term emerged from extensive listening to interview recordings...

33 citations

Journal ArticleDOI
TL;DR: The concepts of complete and partial models are introduced with the goal to study the quality of inference procedures and the added value introduced by probability into model based diagnostics will be discussed.
Abstract: Probabilistic argumentation systems are based on assumption-based reasoning for obtaining arguments supporting hypotheses and on probability theory to compute probabilities of supports. Assumption-based reasoning is closely related to hypothetical reasoning or inference through theory formation. The latter approach has well known relations to abduction and default reasoning. In this paper assumption-based reasoning, as an alternative to theory formation aiming at a different goal, will be presented and its use for abduction and model-based diagnostics will be explained. Assumption-based reasoning is well suited for defining a probability structure on top of it. On the base of the relationships between assumption-based reasoning on the one hand and abduction on the other hand, the added value introduced by probability into model based diagnostics will be discussed. Furthermore, the concepts of complete and partial models are introduced with the goal to study the quality of inference procedures. In particular this will be used to compare abductive to possible explanations.

33 citations

Journal ArticleDOI
TL;DR: It is argued that it is possible to argue reasonably for and against arguments from classifications and definitions, provided they are seen as defeasible (subject to exceptions and critical questioning), and how such schemes can be identified with heuristics, or short-cut solutions to a problem.
Abstract: We contend that it is possible to argue reasonably for and against arguments from classifications and definitions, provided they are seen as defeasible (subject to exceptions and critical questioning). Arguments from classification of the most common sorts are shown to be based on defeasible reasoning of various kinds represented by patterns of logical reasoning called defeasible argumentation schemes. We show how such schemes can be identified with heuristics, or short-cut solutions to a problem. We examine a variety of arguments of this sort, including argument from abductive classification, argument from causal classification, argument from analogy-based classification and arguments from classification based on generalizations.

33 citations


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