FEATURE
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GO WAS CONSIDERED THE HOLY GRAIL OF GAME AI
networks have been around for decades, it has only been in the last few years that their potential has been realised, as the amount of available training data has skyrocketed and computing power, along with dedicated hardware, has become more readily available.
AlphaGo
Go is an ancient strategy game for two players, who take turns placing black and white pieces (stones) on a 19x19 board. It is a notoriously difficult game for computers. The number of possible board positions is estimated at an astronomical 10170. Traditional search techniques in such a vast space of possibilities is pointless and it has proved very hard to develop functions
n Neural networks are behind nearly all AI technology
that reliably evaluate positions in order to guide the search. Researchers estimated that it might take decades for machines to beat humans at Go, which was considered to be the holy grail of game AI. Enter AlphaGo, a computer program by DeepMind. In 2017, AlphaGo beat Ke Jie, the world’s top-ranked player at the time, following victories over other high-ranking professional players. AlphaGo combines previously known methods in a novel way. It studies human
games or uses self-play, in order to learn how to evaluate positions and moves. It uses neural networks to compute its evaluation functions and modify them while learning. It searches through the vast space of possible positions by taking random samples, instead of searching systematically. This is such a promising generic approach that AlphaZero, a generalised version of the program, used only self-play to achieve a superhuman level of play in the games of chess, Shogi and Go within 24 hours. This is a step closer to Turing’s vision of a blank slate child-programme, endowed with the ability to learn.
FURTHER READING Artificial Intelligence, Michael Wooldridge. Ladybird, 2019.
Machine Learning for Humans, Vishal Maini and Samer Sabri, 2017.
The Quest for Artificial Intelligence:
A History of Ideas and Achievements.
Nils J. Nilsson, Cambridge University Press, 2010.
Machines Who Think, Pamela McCorduck, A K Peters, 2004.
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