Parallel Learning of Automatic Classes of Languages
We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite 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 difference n − m, the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open.
Jain, S., Kinber, E. (2014). Parallel Learning of Automatic Classes of Languages. In Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles (Eds.), Algorithmic learning theory (pp. 70-84). Basel: Springer.