Building a Simple AI for Playing Chess

Building a simple AI for playing chess involves creating a program that can evaluate board positions and make decisions based on them. A basic approach starts with representing the chessboard using a two-dimensional array, where each piece is identified by unique values. The AI can use a search algorithm, like minimax, which simulates potential moves and their outcomes to determine the best possible move. Enhancements can include implementing a scoring system that evaluates board positions based on piece value, control of the center, and other strategic factors. Adding alpha-beta pruning can optimize the search by eliminating less promising moves, improving efficiency. Finally, integrating a simple opening book can help the AI play stronger in the early game. This foundational setup can be further refined by incorporating machine learning techniques or advanced heuristics for a more sophisticated playstyle.