Read e-book online Abductive Inference: Computation, Philosophy, Technology PDF

By John R. Josephson, Susan G. Josephson

ISBN-10: 0511530129

ISBN-13: 9780511530128

ISBN-10: 0521434610

ISBN-13: 9780521434614

ISBN-10: 0521575451

ISBN-13: 9780521575454

In casual phrases, abductive reasoning contains inferring the simplest or so much believable clarification from a given set of evidence or info. This quantity offers new principles approximately inferential and information-processing foundations for wisdom and walk in the park. The authors argue that wisdom arises from adventure via techniques of abductive inference, unlike the view that it arises noninferentially, or that deduction and inductive generalization are adequate to account for wisdom. The e-book tells the tale of six generations of more and more refined usual abduction machines and the invention of reasoning innovations that make it computationally possible to shape well-justified composite explanatory hypotheses, regardless of the specter of combinatorial explosion. This ebook might be of serious curiosity to researchers in AI, cognitive technology, and philosophy of technological know-how.

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However, something is wrong with this analysis. To see this, consider the alternative analysis of inductive projections, as follows: Conceptual analysis of abduction 23 Observations -» At least generally A's are ZTs -> The next A will be a 5. This inference is stronger in that it establishes its conclusion with more certainty, which it does by hedging the generalization and thus making it more plausible, more likely to be true. It could be made stronger yet by hedging the temporal extent of the generalization: Observations -> At least generally A's are ZTs, at least for the recent past and the immediate future —> The next A will be a B.

Like other good art, this sort of AI is a stimulus for discussions of human nature and the human condition. AI as art serves as a focal point for criticisms of the very project of making thinking machines. That there are such criticisms shows the vitality of this sort of AI. However, these criticisms are completely inapplicable to AI as science and engineering. Whether machines can have ac- Knowledge-based systems and the science ofAI tual humanlike cognitive states is irrelevant to modeling human intelligence in cooperation with cognitive psychology, to building information-processing systems, and to using AI technology in business and industry.

See Salmon, 1967, p. ) Is the likelihood that the next patient has the flu best estimated based on the frequency in all the people in the world over the entire history of medicine? It seems better at least to control for the season and to narrow the class to include people at just this particular time of the year. ) Furthermore, each flu season is somewhat different, so we would do better to narrow to considering people just this year. Then, of course, the average patient is not the same as the average person, and so forth, so the class should probably be narrowed further to something such as this: people of this particular age, race, gender, and social status who have come lately to doctors of this sort.

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Abductive Inference: Computation, Philosophy, Technology by John R. Josephson, Susan G. Josephson


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