Smart Chess Assistant: Using AI to See the Board and Suggest the Best Moves

In this study we present such an artificial intelligence driven system capable of determining the chess pieces on a physical board and providing recommendations of the best move, given the current game position. The system employs the combination of YOLOv8 object detection algorithm for accurate piece recognition and Stockfish chess engine for strategic move recommendations which allows it to run time analysis of gameplay in a real time basis. We train the YOLOv8 model on a chess image dataset with 606 chess images tagged, and thus detect with precision between 91% and 98% depending on the piece. It looks at the application of deep learning technology in the analysis of chess and its key role for automated systems to both improve and compete in educational and competitive play. Through this work, we show the potential in use of chess with AI technologies and addressing some issues in position recognition accuracy and piece identification. Beyond this, it describes in detail a plan for future improvements, including more advanced image processing techniques and further applications of the system to other board games and other sports. Overall, the results from this research provide evidence of the profound shifts that AI can bring to traditional game play, laying the groundwork for future work in intelligent gaming solutions which can both enhance the game player experience, promote learning, and further enhance the competitive dimension of chess.