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 statistical query model is presented. Extensive experiments demonstrate the efficiency and accuracy of the algorithm.