We introduce and explore a model for parallel learning of families of languages computable by nite automata. In this model, an algorithmic or automatic learner takes on n dierent input languages and identies at least m of them correctly. For nite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the dierence n m, the number of languages which are potentially not identied, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel nite learnability by nite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open.
['Sanjay Jain', 'Efim B. Kinber']
Parallel learning of automatic classes of languages