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Efficient Learning of Typical Finite Automata from Random Walks

1997
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Information and Computation
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This paper describes new and efficient algorithms for learning deterministic finite automata. Our approach is primarily distinguished by two features: (1) the adoption of an average-case setting to model the"typical" labeling of a finite automaton, while retaining a worst-case model for the underlying graph of the automaton, along with (2) a learning model in which the learner is not provided with the means to experiment with the machine, but rather must learn solely by observing the

doi:10.1006/inco.1997.2648
fatcat:cbz4m7x7ujbozlove6gsfrys7q