# Abductive Reasoning and Learning by Peter A. Flach, Antonis C. Kakas (auth.), Dov M. Gabbay,

By Peter A. Flach, Antonis C. Kakas (auth.), Dov M. Gabbay, Rudolf Kruse (eds.)

This publication includes top survey papers at the a number of points of Abduction, either logical and numerical methods. Abduction is important to all parts of utilized reasoning, together with man made intelligence, philosophy of technology, computing device studying, information mining and choice concept, in addition to good judgment itself.

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Extra info for Abductive Reasoning and Learning

Sample text

1987). DEFINITION 1. , 1987] (hypothesis assembly) A domain for hypothesis assembly is defined by the triple (

Remarks: • The definition of explanation comprises the "classical" first-order case, if cp _ = 0 for every explanation. • Minimality of an explanation is defined with respect to subset-minimality of cp_ and cp+. • Update of an autoepistemic theory T is regarded as the construction of (T U cp +) \ cp _ for the minimal explanation (cp +, cp _) of a formula w if w has to be inserted into T, and the minimal anti-explanation (cp+, cp_) if w is deleted. As also the authors state, this approach defines a more general framework than previous ones and better accounts for Peirce's theory of abduction (cf.

A hypothesis CPI, added to H yp as an explanation of the observation WI, can later become superfluous, because hypothesis CP2 explains W2 and WI as well. We get the following procedure: for each cP E H yp doifn' ~ e(Hyp\{cp}) then Hyp f- Hyp \ {cp}; fi od Example (continuation): We had the solution Hyp = {no_bird(F), frog(F)}. , Hyp f- {frog(F)}. Remark: This procedure does not ensure that the smallest solution with respect to set cardinality is found. An algorithm solving this problem in the context of a set-cover-based model was designed by Reggia (cf.