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Released
4/10/2020Skill Level Advanced
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- Playing against the computer is only fun when it's challenging, let alone beating a human player at chess. I'm Eduardo Copeno and I'm excited to tell you all about some of the most popular algorithms behind game playing computers. One of the many exciting areas of artificial intelligence. In this course we'll go over some of the techniques implemented by Deep Blue, the IBM computer that defeated the chess world champion, Garry Kasparov back in 1997. You'll also get to see these techniques in a fun implementation of the Cat Drop game, developed in Python. Plus you'll have the chance to play around with the code. So welcome to the course. I'm sure you'll find these clever tricks interesting.
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Introduction
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What you should know1m 6s
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1. Turn-Based Games
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Some history as motivation3m 46s
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Different types of games2m 17s
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Tree-based decision-making2m 28s
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Time complexity of chess2m 31s
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The cat trap game3m 36s
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2. The Minimax Algorithm
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Minimax overview4m 1s
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The minimax algorithm3m 41s
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A word on complexity2m 46s
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Alpha-beta pruning5m 32s
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Code example: A pruning cat3m 25s
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3. Depth-Limited Search
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Depth-limited search3m 50s
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Challenge solution3m 44s
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4. Iterative Deepening
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5. Fun with Optimizations
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The negamax algorithm4m 7s
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Transposition tables2m 2s
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Conclusion
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Next steps2m 14s
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Video: Playing against a computer is only fun when it's challenging