The paper introduced AlphaGo, the first program to defeat a human professional Go player without handicap. It combined deep neural networks — trained with supervised learning and reinforcement learning — with Monte Carlo tree search (MCTS), enabling efficient move selection and board evaluation in Go’s massive search space. AlphaGo’s victory against European champion Fan Hui marked a historic AI milestone, showcasing that combining learning-based policies with search can surpass prior handcrafted methods, reshaping both game AI and broader AI research directions.
