The document discusses using a neural network and backpropagation algorithm to teach an AI agent to play the chess endgame of King-Rook vs King. Sample positions and moves were generated from a chess program to create a training dataset. Neural networks with varying hidden nodes were trained on the data. Networks with more hidden nodes, regularization, and bias performed better, generalizing moves and avoiding overfitting. The approach demonstrated the feasibility of an AI learning chess endgames from examples rather than requiring expert knowledge engineering.