Mind Change Complexity of Learning Logic Programs

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Mind Change Complexity of Learning Logic Programs
The present paper motivates the study of mind change complexity for learning minimal models of length-bounded logic programs. It establishes ordinal mind change complexity bounds for learnability of these classes both from positive facts and from positive and negative facts. Building on Angluin’s notion of finite thickness and Wright’s work on finite elasticity, Shinohara defined the property of bounded finite thickness to give a sufficient condition for learnability of indexed families of computable languages from positive data. This paper shows that an effective version of Shinohara’s notion of bounded finite thickness gives sufficient conditions for learnability with ordinal mind change bound, both in the context of learnability from positive data and for learnability from complete (both positive and negative) data. Let ω be a notation for the first limit ordinal. Then, it is shown that if a language defining framework yields a uniformly decidable family of languages...
Sanjay Jain, Arun Sharma
Added 04 Aug 2010
Updated 04 Aug 2010
Type Conference
Year 1999
Authors Sanjay Jain, Arun Sharma
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