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