Monotonic versus non-monotonic language learning
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In the present paper strong-monotonic, monotonie and weak-monotonic reasoning is studied in the context of algorithmic language learning theory from positive as well as from positive and negative data.
Strong-monotonicity describes the requirement to only produce better and better generalizations when more and more data are fed to the inference device. Monotonic learning reflects the eventual interplay between generalization and restriction during the process of inferring a language. However, it is demanded that for any two hypotheses the one output later has to be at least as good as the previously produced one with respect to the language to be learnt. Weakmonotonicity is the analogue of cumulativity in learning theory.
We relate all these notions one to the other as well as to previously studied modes of identification, thereby in particular obtaining a strong hierarchy.
KeywordsInitial Segment Recursive Function Inductive Inference Regular Language Positive Data
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