Authors: Christoph Globig, Klaus P. Jantke, Steffen Lange, and Yasubumi Sakakibara.
Source: New Generation Computing Vol. 15, No. 1, 1997, 59-85.
Case-based reasoning is deemed an important technology to
alleviate the bottleneck of knowledge acquisition in artificial intelligence (AI). In
case-based reasoning, knowledge is represented in the form of particular cases
with an appropriate similarity measure rather than any form of rules. The
case-based reasoning paradigm adopts the view that an AI system is dynamically
changing during its life-cycle which immediately leads to learning
Within the present paper, we investigate the problem of case-based learning of indexable classes of formal languages. Prior to learning considerations, we study the problem of case-based representability and show that every indexable class is case-based representable with respect to a fixed similarity measure. Next, we investigate several models of case-based learning and systematically analyze their strength as well as their limitations. Finally, the general approach to case-based learnability of indexable classes of formal languages is prototypically applied to so-called containment decision lists, since they seem particularly tailored to case-based knowledge processing.
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