Learning Automaton

Learning automata are adaptive systems that learn optimal actions through interactions with an environment, aiming to improve decision-making in uncertain or unknown settings. Current research focuses on enhancing learning efficiency and scalability through novel architectures like Tsetlin machines and DeepDFA, as well as integrating learning automata with other techniques such as large language models and reinforcement learning for improved performance and interpretability. These advancements are impacting diverse fields, including robotics, cybersecurity (e.g., defending against blockchain attacks), and hardware design, by enabling more robust, efficient, and explainable AI systems.