By Peter A. Flach, Antonis C. Kakas (auth.), Peter A. Flach, Antonis C. Kakas (eds.)

ISBN-10: 9048154332

ISBN-13: 9789048154333

ISBN-10: 9401706069

ISBN-13: 9789401706063

From the very starting in their research of human reasoning, philosophers have pointed out different kinds of reasoning, along with deduction, which we now name abduction and induction. Deduction is now particularly good understood, yet abduction and induction have eluded an analogous point of figuring out. The papers gathered the following tackle the connection among abduction and induction and their attainable integration. The technique is usually philosophical, occasionally that of natural good judgment, and a few papers undertake the extra task-oriented technique of AI.
The booklet will command the eye of philosophers, logicians, AI researchers and laptop scientists in general.

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Extra resources for Abduction and Induction: Essays on their Relation and Integration

Example text

C. KAKAS from the language of the problem domain (rather than a given theory of the domain), in a process of iteratively improving a hypothesis to meet the various requirements posed by the problem. Furthermore, in induction the comparison of the different possible hypotheses plays a prominent and dynamic role in the actual process of hypothesis generation, whereas in abduction evaluation of the different alternative hypothesis may be done after these have been generated. It should be noted, however, that the observed computational differences between generating abductive hypotheses and generating inductive hypotheses are likely to become smaller once more complex abductive hypotheses are allowed.

This specification can be seen as sample-to-population inference. For example, in Peirce's bean example (p. 5), B is 'these beans are from this bag' (instance knowledge), 0 is 'these beans are white' (observation), and H- 'all the beans from this bag are white' - is satisfied by the model containing 'these beans' as the only beans in the universe. Under the assumption that the population is similar to the sample, we achieve generalisation by restricting attention to formulae true in the sample.

Brother(john,dan). The observation is red_spots (john). A possible explanation for this observation is measles (john). While this explanation is clearly completing instance knowledge and thus abductive, adding it to our theory will lead to the prediction ABDUCTIVE AND INDUCTIVE REASONING: BACKGROUND AND ISSUES 17 red_spots (dan). Thus, the hypothesis that John has measles also seems to qualify as a generalisation. We would argue however that this generalisation effect is already present in the background theory.

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Abduction and Induction: Essays on their Relation and Integration by Peter A. Flach, Antonis C. Kakas (auth.), Peter A. Flach, Antonis C. Kakas (eds.)


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