Professor Arthur L. Samuel

Samuel is most known within the AI community for his groundbreaking work in computer checkers. He thought that teaching computers to play games was very fruitful for developing tactics appropriate to general problems, and he chose checkers because it is relatively simple, but has a depth of strategy. The main driver of the machine was a search tree of the board positions reachable from the current state. Since he had only a very limited amount of available computer memory, Samuel implemented what is now called alpha-beta pruning. Instead of searching each path until it came to the game?s conclusion, Samuel developed a scoring function based on the position of the board at any given time. This function tried to measure the chance of winning for each side at the given position. It took into account such things as the number of pieces on each side, the number of kings, and the proximity of pieces to being ?kinged?. The program chose its move based on a minimax strategy, meaning it made the move that optimized the value of this function, assuming that the opponent was trying to optimize the value of the same function from its point of view.