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.
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
