Go Strength Imitation and Playstyle Recognition Competition in AICUP

Abstract

This paper presents a Go AI competition that aimed to advance AI research by developing models capable of mimicking human Go playing styles and recognizing different playing patterns within game records. The competition challenged participants to create AI models that could accurately replicate the playing styles of professional Go players at various skill levels, as well as classify specific moves based on different playing styles. Participants’ models were evaluated on their ability to accurately imitate professional players and recognize playing styles. The results showed that the top-performing teams leveraged advanced techniques from state-of the-art Go AI systems, such as AlphaGo and AlphaZero, combined with deep learning architectures like convolutional neural networks (CNNs) and residual networks (ResNets). This competition highlights the potential of AI in replicating complex human behaviors and underscores the significance of large-scale datasets and advanced deep learning techniques in achieving this goal.