By John R. Josephson, Susan G. Josephson
In casual phrases, abductive reasoning comprises inferring the easiest or so much believable clarification from a given set of evidence or facts. This quantity offers new rules approximately inferential and information-processing foundations for wisdom and sure bet. The authors argue that wisdom arises from adventure through strategies of abductive inference, unlike the view that it arises noninferentially, or that deduction and inductive generalization are adequate to account for wisdom. The booklet tells the tale of six generations of more and more refined primary abduction machines and the invention of reasoning recommendations that make it computationally possible to shape well-justified composite explanatory hypotheses, regardless of the specter of combinatorial explosion. This e-book could be of serious curiosity to researchers in AI, cognitive technological know-how, and philosophy of technological know-how.
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For playing blackjack). However, for the most part numerical confidence estimates are un- Conceptual analysis of abduction 27 available and unnecessary for reasoning. People are good abductive reasoners without close estimates of confidence. In fact it can be argued that, if confidences need to be estimated closely, then it must be that the best hypothesis is not much better than the next best, in which case no conclusion can be confidently drawn because the confidence of an abductive conclusion depends on how decisively the best explanation surpasses the alternatives.
What may we conclude about the relationship between Mario's pizza and Konrad's stomachache? That Mario's pizza makes Konrad have stomachaches. We may predict that Konrad will have a stomachache after his next visit, too. A good way to understand what is occurring in this example is by way of abduction. After Konrad's first two visits we could not conclude anything because we did not have enough evidence to distinguish between the two competing general hypotheses: 1. , merely coincidental or spurious [say, for example, that on the first visit the stomach ache was caused by a virus contracted elsewhere and that on the second visit it was caused by an argument with his mother]).
Knowledge representation has been a major concern of AI and of work on knowledge-based systems. The general assumption, and consequently the methodology, has been that there is something called domain knowledge that needs to be acquired - quite independent of the problems one might wish to solve - and that the role of a knowledge representation formalism is to help encode it. Connectionist networks, logics of various kinds, frame representations, and rule-based languages (all of which are discussed later in this chapter) have been popular proposals for families of knowledge representation formalisms.