David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Cambridge University Press (1994)
In informal terms, abductive reasoning involves inferring the best or most plausible explanation from a given set of facts or data. It is a common occurrence in everyday life and crops up in such diverse places as medical diagnosis, scientific theory formation, accident investigation, language understanding, and jury deliberation. In recent years, it has become a popular and fruitful topic in artificial intelligence research. This volume breaks new ground in the scientific, philosophical, and technological study of abduction. It presents new ideas about inferential and information-processing foundations for knowledge and certainty. The authors argue that knowledge arises from experience by processes of abductive inference, in contrast to the view that it arises non-inferentially, or that deduction and inductive generalization are enough to account for knowledge. Much AI research is hypothetical, so the importance of this book is that it reports key discoveries about abduction that have been made as a result of designing, building, testing, and analyzing actual working knowledge-based systems for medical diagnosis and other abductive tasks. The book tells the story of six generations of increasingly sophisticated generic abduction machines, RED-1, RED-2, PEIRCE, MDX2, TIPS, QUAWDS, and the discovery of reasoning strategies that make it computationally feasible to form well-justified composite explanatory hypotheses, despite the threat of combinatorial explosion. The final chapter argues that perception is logically abductive and presents a layered-abduction computational model of perceptual information processing. This book will be of great interest to researchers in AI, cognitive science, and philosophy of science.
|Keywords||Abduction (Logic Inference Knowledge, Theory of|
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|Buy the book||$17.02 used (89% off) $38.00 new (37% off) $59.99 direct from Amazon Amazon page|
|Call number||BC199.A26.J67 1994|
|ISBN(s)||0521434610 0521575451 9780521575454|
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Citations of this work BETA
Jun-Young Oh (2014). Understanding Natural Science Based on Abductive Inference: Continental Drift. [REVIEW] Foundations of Science 19 (2):153-174.
Colin W. Evers & W. U. H. (2006). On Generalising From Single Case Studies: Epistemological Reflections. Journal of Philosophy of Education 40 (4):511–526.
David C. Gooding (2010). Visualizing Scientific Inference. Topics in Cognitive Science 2 (1):15-35.
Douglas Walton (2013). An Argumentation Model of Forensic Evidence in Fine Art Attribution. AI and Society 28 (4):509-530.
Jungsub Kim & Donald J. Cunningham (2003). A Syllogism for Formulating Hypotheses. Semiotica 2003 (144).
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