The best Go bot approach (as of some years ago, but it's not like neural networks are a new idea) uses a very different strategy. Specifically, the strategy of "identify a few possible moves, simulate the game for several steps after each move using a very stupid move-making heuristic instead of using this actual strategy recursively, and then pick the move that yielded the best simulated board state".
The "use a stupid heuristic as part of the evaluation function" is is, in fact, also an important part of Chess AI's mode (as Quiescence Search), through for different reasons.
Comments
The best Go bot approach (as of some years ago, but it's not like neural networks are a new idea) uses a very different strategy. Specifically, the strategy of "identify a few possible moves, simulate the game for several steps after each move using a very stupid move-making heuristic instead of using this actual strategy recursively, and then pick the move that yielded the best simulated board state".
Monte Carlo Tree Search ( Random playout ) is currently the best computer strategy for evaluating a Go position.
This is likely due to the way Go works , random playout provides a rough estimate of who controls what territory ( this is how Go is scored ).
Recently two deep-learning papers showed very impressive results.
http://arxiv.org/abs/1412.3409
http://arxiv.org/abs/1412.6564
The neural networks were tasked with predicting what move an expert would make given a position.
The MCTS takes a long time 100,000 playouts are typical - once trained the neural nets are orders of magnitude faster.
The neural nets output a probability for each move ( that an expert would make that move ) - all positions are evauluated in a single forward pass.
Current work centers around combining the two approaches, MCTS evaluates the best suggestions from the neural net.
Expert Human players are still unbeatable by computer Go.
For Chess see David Silver's work on TreeStrap
It learns to master level from self-play.
http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Applications_fil...
also his lecture bootstrapping from tree based search
http://www.cse.unsw.edu.au/~cs9414/15s1/lect/1page/TreeStrap...
and Silver's overview on board game learning
http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching_files/g...
The "use a stupid heuristic as part of the evaluation function" is is, in fact, also an important part of Chess AI's mode (as Quiescence Search), through for different reasons.