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STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
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Journal of Computer and System Sciences - 3rd Annual Conference on Structure in Complexity Theory, June 14–17, 1988
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Journal of the ACM (JACM)
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STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Machine Learning
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
Looping suffix tree-based inference of partially observable hidden state
ICML '06 Proceedings of the 23rd international conference on Machine learning
Interactive presentation: Automatic model generation for black box real-time systems
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ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
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IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Information and Computation
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TbiLLC'11 Proceedings of the 9th international conference on Logic, Language, and Computation
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IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We present new procedures for inferring the structure of a finite-state automaton (FSA) from its input/output behavior, using access to the automaton to perform experiments.Our procedures use a new representation for finite automata, based on the notion of equivalence between tests. We call the number of such equivalence classes the diversity of the automaton; the diversity may be as small as the logarithm of the number of states of the automaton. For the special class of permutation automata, we describe an inference procedure that runs in time polynomial in the diversity and log(1/&dgr;), where &dgr; is a given upper bound on the probability that our procedure returns an incorrect result. (Since our procedure uses randomization to perform experiments, there is a certain controllable chance that it will return an erroneous result.) We also discuss techniques for handling more general automata.We present evidence for the practical efficiency of our approach. For example, our procedure is able to infer the structure of an automaton based on Rubik's Cube (which has approximately 1019 states) in about 2 minutes on a DEC MicroVax. This automaton is many orders of magnitude larger than possible with previous techniques, which would require time proportional at least to the number of global states. (Note that in this example, only a small fraction (10-14) of the global states were even visited.)Finally, we present a new procedure for inferring automata of a special type in which the global state is composed of a vector of binary local state variables, all of which are observable (or visible) to the experimenter. Our inference procedure runs provably in time polynomial in the size of this vector (which happens to be the diversity of the automaton), even though the global state space may be exponentially larger. The procedure plans and executes experiments on the unknown automaton; we show that the number of input symbols given to the automaton during this process is (to within a constant factor) the best possible.