Learning DFA from Correction and Equivalence Queries
In active learning, membership queries and equivalence que- ries have established themselves as the standard combination to be used. However, they are quite “unnatural” for real learning environments (membership queries are oversimplified and equivalence queries do not have a correspondence in a real life setting). Based on several linguistic arguments that support the presence of corrections in children’s language acquisition, we propose another kind of query called correction query. We provide an algorithm that learns DFA using correction and equivalence queries in polynomial time. Despite the fact that the worst case complexity of our algorithm is not better than Angluin’s algorithm, we show through a large number of experiments that the average number of queries is considerably reduced by using correction queries.
KeywordsActive learning learning DFA membership query equivalence query correction query
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