The minimum consistent DFA problem cannot be approximated within and polynomial
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Theoretical Computer Science
Characteristic Sets for Polynomial Grammatical Inference
Machine Learning
Polynomial Distinguishability of Timed Automata
ICGI '08 Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications
Inference of event-recording automata using timed decision trees
CONCUR'06 Proceedings of the 17th international conference on Concurrency Theory
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Learning driving behavior by timed syntactic pattern recognition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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We study the complexity of identifying (learning) timed automata in the limit from data. In previous work, we showed that in order for timed automata to be efficiently identifiable in the limit, it is necessary that they are deterministic and that they use at most one clock. In this paper, we show this is also sufficient: we provide an algorithm that identifies one-clock deterministic timed automata efficiently in the limit.