A Unifying Approach to Monotonic Language Learning on Informant

The present paper deals with strong-monotonic, monotonic and weak-monotonic language learning from positive and negative examples. The three notions of monotonicity re ect di erent 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. We characterize strong-monotonic, monotonic, weak-monotonic and 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.