#28: Arthur Samuel’s Checkers Program

How did one of the very first machine learning programs actually work? This episode dives into the mechanics of Arthur Samuel's famous 1950s checkers-playing AI.

 

In This Episode, You'll Learn:

 

• The checkers program used a combination of a scorecard (to rate board positions) and a look-ahead function (to explore future moves) to choose the best action.

• The system learned through reinforcement learning; moves that led to wins were positively reinforced, causing the scorecard's feature weights to be adjusted automatically over hundreds of games.

• The program utilized the minimax strategy—choosing the move that maximizes its own score while minimizing the maximum damage the opponent could inflict.

• A technique called pruning was used to dramatically increase the search depth by intelligently skipping unproductive branches, a concept vital for efficiency in modern computing.

This weeks poll: Checkers or Chess