Communications of the ACM
Learning regular sets from queries and counterexamples
Information and Computation
Inference of finite automata using homing sequences
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Negative Results for Equivalence Queries
Machine Learning
Learning DFA from correction and equivalence queries
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
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We present a new learning algorithm introducing a helpful teacher who models the learners' knowledge. Our algorithm, called learning from extensions (LEX), learns finite-state transducers using only one type of query called extension query. Our query was inspired by equivalence queries and counterexamples, but we show in this article that it is possible to learn efficiently finite state transducers using only extension queries (it is known that only with membership queries or only with equivalence queries is not possible). The teacher answers an extension query by connecting the new information asked by the learner with the information that the learner already knows. We prove that our new algorithm LEX discovers a target finite-state transducer in polynomial time. We also discuss briefly several complexity aspects and we give an example.