MiniMaxing is a chess AI platform where instead of playing against other players and moving yourself, you code an AI bot and challenge other player’s AI. The inital project will have an optimized minimax algorithm in the backend with heuristics and alpha-beta pruning, and the player’s objective is to make this algorithm as good as possible by writing the point evaluator. In minimax it requires giving a number to each game state and then backtracking. In some games this is easy, like tic-tac-toe where the possibilities are small and you can just search until the winning state. But in chess since this is exponential growth, that would be too inefficient. So you have to assign point values to intermediate states. But how do you assign points? Chess isn’t just about raw point values of pieces and capturing them, position matters. That is the players job, given a set number of rules, they can write those rules in python such that they return a boolean, and give them a weight.
Used ChatGPT (Codex) to help debug some of my code.
