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.
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...
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.