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...

• Type: Journal
• Year: 2015
• Where: ALT
• Authors: Dana Angluin, Dongqu Chen
• Keywords: ALT 2015, Machine Learning