Non-monotonic Learning

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Abstract
This paper addresses methods of specializing first-order theories within the context of incremental learning systems. We demonstrate the short comings of existing first-order incremental learning systems with regard to their specialization mechanisms. We prove that these shortcomings are fundamental to the use of classical logic. In particular, minimal ‘correcting’ specializations are not always obtainable within this frame work. We propose instead the adoption of a specialization scheme based on an existing non-monotonic logic formalism.

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