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By John R. Josephson, Susan G. Josephson

In casual phrases, abductive reasoning contains inferring the easiest or such a lot believable rationalization from a given set of evidence or info. This quantity offers new rules approximately inferential and information-processing foundations for wisdom and walk in the park. The authors argue that wisdom arises from event via techniques of abductive inference, not like the view that it arises noninferentially, or that deduction and inductive generalization are adequate to account for wisdom. The publication tells the tale of six generations of more and more subtle known abduction machines and the invention of reasoning options that make it computationally possible to shape well-justified composite explanatory hypotheses, regardless of the specter of combinatorial explosion. This e-book can be of serious curiosity to researchers in AI, cognitive technological know-how, and philosophy of technology.

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Extra info for Abductive Inference: Computation, Philosophy, Technology

Example text

As opposed to H " discernible in the background. Abduction is a process of going from some Given to a best explanation for that (or related) given. Describing a computational process as abduction says what it accomplishes - namely, generation, criticism, and acceptance of an explanation - but superficially it says nothing about how it is accomplished (for example, how hypotheses are generated or how generation interacts with acceptance). An explanation is an assignment of causal responsibility; it tells a causal story (at least this is the sense of "explanation" relevant to abduction).

One thing seems clear: Predictions from hedged generalizations are not deductions. Predictions from hedged generalizations belong to the same family as statistical syllogisms which have forms like these: 14 m/n of the A's are B's (where m/n > 1/2). Therefore, the next A will be a B. and m/n of the ^ ' s are B's. Therefore, approximately m/n of the A's in the next sample will be B's. These are also related to the following forms: Generally A's are B's. S is an A. Therefore, S is a B. and A typical, normal X does Y.

These pieces help constrain the problem-solving effort and direct it toward potentially useful intermediate states. These pieces of knowledge are domain specific in the sense that they are not general principles or heuristics that apply in many problem domains, but rather they are stated directly in the form of appropriate actions to try in various situations. An alternative to this position is that the complex problem-solving processes operate on a combination of the basic knowledge that defines the domain together with various forms of general and commonsense knowledge.

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