Abductive Reasoning Algorithm
An Abductive Reasoning Algorithm is a Reasoning Algorithm that uses abductive operations to produce an abductive argument.
- Context:
- It can (typically) apply nearly sufficient logic operations, such as affirming the consequent.
- It can be implemented into an Abductive Reasoning System to solve a Abductive Reasoning Task.
- …
- Example(s):
- Counter-Example(s):
- See: Heuristic Reasoning, Non-Monotonic Reasoning, Most Economical Explanation, Explanation-based Learning, Explanation-Based Learning; Inductive Logic Programming, Set Cover Problem, Occam's Razor.
References
2019
- (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Abductive_reasoning Retrieved:2019-9-1.
- Abductive reasoning (also called abduction,[1] abductive inference, or retroduction ) is a form of logical inference which starts with an observation or set of observations then seeks to find the simplest and most likely explanation for the observations. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it. Abductive conclusions are thus qualified as having a remnant of uncertainty or doubt, which is expressed in retreat terms such as "best available" or "most likely." One can understand abductive reasoning as inference to the best explanation, although not all usages of the terms abduction and inference to the best explanation are exactly equivalent. In the 1990s, as computing power grew, the fields of law, [2] computer science, and artificial intelligence research [3] spurred renewed interest in the subject of abduction.
Diagnostic expert systems frequently employ abduction.
- Abductive reasoning (also called abduction,[1] abductive inference, or retroduction ) is a form of logical inference which starts with an observation or set of observations then seeks to find the simplest and most likely explanation for the observations. This process, unlike deductive reasoning, yields a plausible conclusion but does not positively verify it. Abductive conclusions are thus qualified as having a remnant of uncertainty or doubt, which is expressed in retreat terms such as "best available" or "most likely." One can understand abductive reasoning as inference to the best explanation, although not all usages of the terms abduction and inference to the best explanation are exactly equivalent. In the 1990s, as computing power grew, the fields of law, [2] computer science, and artificial intelligence research [3] spurred renewed interest in the subject of abduction.
- ↑ For example:
- ↑ See, e.g. Analysis of Evidence, 2d ed. by Terence Anderson (Cambridge University Press, 2005)
- ↑ For examples, see "Abductive Inference in Reasoning and Perception", John R. Josephson, Laboratory for Artificial Intelligence Research, Ohio State University, and Abduction, Reason, and Science. Processes of Discovery and Explanation by Lorenzo Magnani (Kluwer Academic/Plenum Publishers, New York, 2001).
2017a
- (Stanford Encyclopedia of Philosophy, 2017) ⇒ Stanford Encyclopedia of Philosophy: Abduction http://plato.stanford.edu/entries/abduction/ First published Wed Mar 9, 2011; substantive revision Fri Apr 28, 2017
- QUOTE: In the philosophical literature, the term “abduction” is used in two related but different senses. In both senses, the term refers to some form of explanatory reasoning. However, in the historically first sense, it refers to the place of explanatory reasoning in generating hypotheses, while in the sense in which it is used most frequently in the modern literature it refers to the place of explanatory reasoning in justifying hypotheses. In the latter sense, abduction is also often called “Inference to the Best Explanation.”
This entry is exclusively concerned with abduction in the modern sense, although there is a supplement on abduction in the historical sense, which had its origin in the work of Charles Sanders Peirce — see the Supplement: Peirce on Abduction. See also the entry on scientific discovery, in particular the section on discovery as abduction.
Most philosophers agree that abduction (in the sense of Inference to the Best Explanation) is a type of inference that is frequently employed, in some form or other, both in everyday and in scientific reasoning. However, the exact form as well as the normative status of abduction are still matters of controversy. This entry contrasts abduction with other types of inference; points at prominent uses of it, both in and outside philosophy; considers various more or less precise statements of it; discusses its normative status; and highlights possible connections between abduction and Bayesian confirmation theory.
- QUOTE: In the philosophical literature, the term “abduction” is used in two related but different senses. In both senses, the term refers to some form of explanatory reasoning. However, in the historically first sense, it refers to the place of explanatory reasoning in generating hypotheses, while in the sense in which it is used most frequently in the modern literature it refers to the place of explanatory reasoning in justifying hypotheses. In the latter sense, abduction is also often called “Inference to the Best Explanation.”
2017b
- (Kakas, 2017) ⇒ Antonis C. Kakas. (2017). “Abduction”. In: (Sammut & Webb, 2017) DOI:10.1007/978-1-4899-7687-1_1
- QUOTE: Abduction is a form of reasoning, sometimes described as “deduction in reverse,” whereby given a rule that “A follows from B” and the observed result of “A” we infer the condition “B” of the rule. More generally, given a theory, T, modeling a domain of interest and an observation, “A,” we infer a hypothesis “B” such that the observation follows deductively from T augmented with “B.” We think of “B” as a possible explanation for the observation according to the given theory that contains our rule. This new information and its consequences (or ramifications) according to the given theory can be considered as the result of a (or part of a) learning process based on the given theory and driven by the observations that are explained by abduction. Abduction can be combined with induction in different ways to enhance this learning process.