Abduction

Abduction
(A subtopic ofReasoning)
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"What is Abduction? What I call Abduction or Inference to the Best Explanation is a form of inference that follows a pattern like this:
D is a collection of data (facts, observations, givens),
H explains D (would, if true, explain D),
No other hypothesis explains D as well as H does.
Therefore, H is probably correct."
- From Abductive Inference in Reasoning and Perception, by John Josephson, Ohio State University Computer and Information Science Department Laboratory for Artificial Intelligence Research (LAIR). Be sure to the links to related projects,publications, andAbductive Inference, Computation, Philosophy, Technology. Edited by John R. Josephson and Susan G. Josephson. 199., New York, Cambridge University Press.
"Abduction is inference to the best explanation and has applications to diagnosis, plan recognition, natural language understanding, vision, and many other tasks. It is frequently formalized as constructing a set of assumptions that logically imply and therefore ‘explain‘ a set of observations. ... Below is an extreme example of abduction from Eugene Ionesco‘s play ‘Rhinoceros‘ from the ‘Theater of the Absurd‘ school:
All cats die.
Socrates is dead.
Therefore, Socrates is a cat."
-Machine Learning Research Group University of Texas at Austin, Department of Computer Sciences, Artifical Intelligence Laboratory. Also see their relatedpublications.

Scientists Develop Experimenting Robot. By Alex Dominguez. Associated Press / available from the Star-Telegram (January 14, 2004). "‘It‘s like if you have a machine which is broken, the system can automatically reason to find all the possible ways it can be broken,‘ said Ross King of the University of Wales-Aberystwyth. ‘Some philosophers have thought this is impossible for computers because that‘s the imaginative leap.‘ The robot scientist uses a type of reasoning called abduction. King said it is the kind of reasoning police use to reconcile clues when investigating a crime. "If this person committed the crime, all the clues make sense," King said." Also see:
A robot that likes to play with test tubes. By David Akin. The Globe & Mail (January 17, 2004). "Special artificial intelligence software was written to give the machine the ability to formulate a scientific hypothesis about genes and the genetic makeup of yeast. In other words, it could learn and apply what it learned to solve new problems or learn new things. Once it formed a hypothesis, it had to figure out how to test its theory. This, usually, is where that human intuition comes in. Scientists, having made a particular observation about the world, are often faced with thousands or even millions of ways of figuring out how the condition they observed came to be. To do this, scientists employ what is know as abduction. Abduction is often described as the reverse of deduction. It is a form of logical reasoning in which one starts with an observation of a certain condition and then tries to isolate the variables or causes of that condition. If I have two marbles and you have three marbles, you may engage in deduction to conclude that there are total of five marbles. But if all we know is that there are five marbles and two of us, you might engage in abduction to form five possible hypotheses about how many marbles each of us has. (I have none, you have five; I have one, you have four; I have two, you have three; and so on.) Scientific problems can frequently generate thousands and even millions of such hypotheses when using this abductive technique. Human investigators frequently use intuition, instinct and a little luck to zero in on a few likely hypotheses that can be tested. The Robot Scientist, too, has a special hypothesis-generation engine built into it so that it can ask the ‘how‘ and the ‘why‘ questions to explain real-world phenomena."Related news articles on our Scientific Discovery page.
What is Abductive Inference? By Uwe Wirth, Frankfurt University. "Abductive reasoning: constitutes according to Peirce the "first stage" of scientific inquiries (CP 6.469) and of any interpretive processes. ‘Abduction‘ is the process of adopting an explanatory hypothesis (CP 5.145) and covers two operations: the selection and the formation of plausible hypotheses. As process of finding premisses, it is the basis of interpretive reconstruction of causes and intentions, as well as of inventive construction of theories. ... Recently the concept of abductive reasoning as reasoning to the best explanation is introduced and discussed in the field of Artificial Intelligence and expert systems (See van der Lubbe 1993). Expert systems aim at imitating the reasoning process and the human faculty to deal with uncertain information in a very efficient way. The question, however, is, how abductive inference as a pragmatic strategy of reasoning can be implemented in expert systems and whether artificial intelligence as a computational automatism can make creative guesses."
Related Pages
LogicScientific Discovery
More Readings
Abduction, Reason, and Science: A Review. By Atocha Aliseda. AI Magazine 23(1): Spring 2002, 113-114. Review of Abduction, Reason, and Science: Processes of Discovery and Explanation, by Lorenzo Magnani, New York, Kluwer Academic/Plenum Publishers, 2001, 205 pages, ISBN 0- 306-46514-0. "Broadly speaking, abduction is a reasoning process invoked to explain a puzzling observation."
Abduction, Experience, and Goals: A Model of Everyday Abductive Explanation. By David B. Leake. The Journal of Experimental and Theoretical Artificial Intelligence. 7:407-428, 1995. 25 pages.Abstract: "Many abductive understanding systems generate explanations by a backwards chaining process that is neutral both to the explainer‘s previous experience in similar situations and to why the explainer is attempting to explain. This article examines the relationship of such models to an approach that uses case-based reasoning to generate explanations."
Learning, Bayesian Probability, Graphical Models, and Abduction. By David Poole. To appear, Peter Flach and Antonis Kakas, editors, Abduction and Induction: essays on their relation and integration, Kluwer, 1998.Abstract: "In this chapter I review Bayesian statistics as used for induction and relate it to logic-based abduction. Much reasoning under uncertainty, including induction, is based on Bayes‘ rule. Bayes‘ rule is interesting precisely because it provides a mechanism for abduction."
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