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2015
Springer

Learning a Random DFA from Uniform Strings and State Information

4 years 10 months ago
Learning a Random DFA from Uniform Strings and State Information
Deterministic finite automata (DFA) have long served as a fundamental computational model in the study of theoretical computer science, and the problem of learning a DFA from given input data is a classic topic in computational learning theory. In this paper we study the learnability of a random DFA and propose a computationally efficient algorithm for learning and recovering a random DFA from uniform input strings and state information in the statistical query model. A random DFA is uniformly generated: for each state-symbol pair (q ∈ Q, σ ∈ Σ), we choose a state q ∈ Q with replacement uniformly and independently at random and let ϕ(q, σ) = q , where Q is the state space, Σ is the alphabet and ϕ is the transition function. The given data are stringstate pairs (x, q) where x is a string drawn uniformly at random and q is the state of the DFA reached on input x starting from the start state q0. A theoretical guarantee on the maximum absolute error of the algorithm in the st...
Dana Angluin, Dongqu Chen
Added 15 Apr 2016
Updated 15 Apr 2016
Type Journal
Year 2015
Where ALT
Authors Dana Angluin, Dongqu Chen
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