[Link] AlphaGo: Mastering the ancient game of Go with Machine Learning

ESRogs

DeepMind's go AI, called AlphaGo, has beaten the European champion with a score of 5-0. A match against top ranked human, Lee Se-dol, is scheduled for March.

Games are a great testing ground for developing smarter, more flexible algorithms that have the ability to tackle problems in ways similar to humans. Creating programs that are able to play games better than the best humans has a long history

[...]

But one game has thwarted A.I. research thus far: the ancient game of Go.

http://googleresearch.blogspot.com/2016/01/alphago-mastering-ancient-game-of-go.html

DeepMind's go AI, called AlphaGo, has beaten the European champion with a score of 5-0. A match against top ranked human, Lee Se-dol, is scheduled for March.

Games are a great testing ground for developing smarter, more flexible algorithms that have the ability to tackle problems in ways similar to humans. Creating programs that are able to play games better than the best humans has a long history

[...]

But one game has thwarted A.I. research thus far: the ancient game of Go.

http://googleresearch.blogspot.com/2016/01/alphago-mastering-ancient-game-of-go.html

Cite? They use the supervised network for policy selection (i.e. tree pruning) which is a critical part of the system.

I'm referring to figure 1a on page 4 and the explanation below. I can't be sure but the self-play should be contributing a large part to the training and can go on and improve the algorithm even if the expert database stays fixed.

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[Link] AlphaGo: Mastering the ancient game of Go with Machine Learning

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[Link] AlphaGo: Mastering the ancient game of Go with Machine Learning

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