{"ID":2852692,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.17386","arxiv_id":"2510.17386","title":"Inference of Deterministic Finite Automata via Q-Learning","abstract":"Traditional approaches to inference of deterministic finite-state automata (DFA) stem from symbolic AI, including both active learning methods (e.g., Angluin's L* algorithm and its variants) and passive techniques (e.g., Biermann and Feldman's method, RPNI). Meanwhile, sub-symbolic AI, particularly machine learning, offers alternative paradigms for learning from data, such as supervised, unsupervised, and reinforcement learning (RL). This paper investigates the use of Q-learning, a well-known reinforcement learning algorithm, for the passive inference of deterministic finite automata. It builds on the core insight that the learned Q-function, which maps state-action pairs to rewards, can be reinterpreted as the transition function of a DFA over a finite domain. This provides a novel bridge between sub-symbolic learning and symbolic representations. The paper demonstrates how Q-learning can be adapted for automaton inference and provides an evaluation on several examples.","short_abstract":"Traditional approaches to inference of deterministic finite-state automata (DFA) stem from symbolic AI, including both active learning methods (e.g., Angluin's L* algorithm and its variants) and passive techniques (e.g., Biermann and Feldman's method, RPNI). Meanwhile, sub-symbolic AI, particularly machine learning, of...","url_abs":"https://arxiv.org/abs/2510.17386","url_pdf":"https://arxiv.org/pdf/2510.17386v1","authors":"[\"Elaheh Hosseinkhani\",\"Martin Leucker\"]","published":"2025-10-20T10:23:36Z","proceeding":"cs.FL","tasks":"[\"cs.FL\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}