September 15, 2015

[quiescent search] "Much as with the human game, the efficiency of the technique improves according to the number of pieces taken, which limit the possible outcomes and captures."

The Stack 'Giraffe' returns to the practical problems which defeated chess researchers who tried to create less 'systematic' opponents in the mid-1990s. by Martin Anderson via slashdot

'A chess computer has taught itself the game and advanced to ‘international master’-level in only three days by adopting a more ‘human’ approach to the game. Mathew Lai, an MsC student at Imperial College London, devised a neural-network-based chess computer dubbed Giraffe [PDF] – the first of its kind to abandon the ‘brute force’ approach to competing with human opponents in favour of a branch-based approach whereby the AI stops to evaluate which of the calculated move branches that it has already made are most likely to lead to victory.

'Most chess computers iterate through millions of moves in order to select their next position, and it was this traditional ‘depth-based’ approach that led to the first ground-breaking robot>human chess victory in 1997, when IBM’s Big Blue beat reigning world champion Garry Kasparov.

'Lai sought instead to create a more evolutional end-to-end AI, building and improving on previous efforts which sought to leverage neural networks, but which paid performance penalties, and faced logical issues about which of the potential millions of ‘move branches’ to explore efficiently.'

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