Abstracting background knowledge for concept learning
In this paper the current approach to automatic concept acquisition is criticized. In particular, it is argued that using only generalization, coupled with a simplicity criterion for selecting hypotheses, generates concept descriptions which are poor with respect to their information content, difficult to agree upon by humans and strictly task dependent.
A new framework is proposed instead, based on notion of abstraction, in the sense of Plaisted and Tenenberg. The novelty of this paper, with respect to previous ones, occasionally mentioning abstraction in machine learning, is that it gives a precise definition of abstraction, shows its relation with generalization and also offers a computational framework. Moreover, a special type of abstraction, particularly useful for the learning task, is defined, and an algorithm for computing it is also presented. This type of abstraction can be applied to a body of background knowledge (domain theory), allowing EBG to be performed in a more synthetic representation space. This transformation can (at least partially) offer a solution to the problem of domain theory intractability.
A complete example of domain theory abstraction is also worked out, for the sake of exemplification.
KeywordsAbstraction concept learning explanation based learning
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