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Download Abduction, Reason and Science - Processes of Discovery and by L. Magnani PDF

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By L. Magnani

This e-book ties jointly the troubles of philosophers of technology and AI researchers, exhibiting for instance the connections among medical considering and medical examiner structures. It lays out an invaluable common framework for dialogue of a number of sorts of abduction. It develops vital principles approximately points of abductive reasoning which were particularly overlooked in cognitive technology, together with using visible and temporal representations and the position of abduction within the withdrawal of hypotheses.

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Additional resources for Abduction, Reason and Science - Processes of Discovery and Explanation

Sample text

According to Laudan, however, the method of proof by eliminative induction, advanced by Bacon and Hooke, was dropped after Condillac, Newton, and LeSage argued successfully that it is impossible to enumerate exhaustively all the hypotheses that could conceivably explain a set of events. The force of the refutation lies in the open-endedness of the language of science. ]. The computational method known as heuristic search is in some sense a revival of those old ideas of induction by elimination, but with machine methods of generation and search substituted for exhaustive enumeration.

If for example such a theory is simpler and explains more significant data than its competitors, then it can be accepted as the best explanation. As already stressed, in accordance with the epistemological model previously illustrated, medical reasoning may be broken down into two different phases: first, patient data is abstracted and used to select hypotheses, that is hypothetical solutions of the patient's probleln (selective abduction phase); second, these hypotheses provide the starting conditions for forecasts of expected consequences which should be compared to the patient's data in order to evaluate (corroborate or eliminate) those hypotheses which they come from (deduction-induction cycle).

On the contrary, in the case of inductive logic programming (ILP) the problem is to "select" a generalizing hypothesis able to entail additional observable information on unobserved individuals (that is predictions), finding new individuals for which the addition of the hypothesis to our knowledge is necessary to derive some observable properties for them (Console and Saitta, 23 24 Cfr. also the clear article by Console, Theseider Dupre, Torasso, 1991. An overview on the relationships between abduction and induction is given in Bessant (2000).

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