Characterization of language learning front informant under various monotonicity constraints

The present paper deals with monotonic and dual monotonic language learning from positive and negative examples. The three notions of monotonicity re BLOCKINect dierent formalizations of the requirement that the learner has to produce always better and better generalizations when fed more and more data on the concept to be learnt. The three versions of dual monotonicity describe the concept that the inference device has to produce exclusively specializations that t better and better to the target language. We characterize strong{monotonic, monotonic, weak{monotonic, dual strong{monotonic, dual monotonic and dual weak{monotonic as well as nite language learning from positive and negative data in terms of recursively generable nite sets. Thereby, we elaborate a unifying approach to monotonic language learning by showing that there is exactly one learning algorithm which can perform any monotonic inference task.