  Monte Carlo tree search (MCTS) is extremely popular in computer Go which
determines each action by enormous simulations in a broad and deep search tree.
However, human experts select most actions by pattern analysis and careful
evaluation rather than brute search of millions of future nteractions. In this
paper, we propose a computer Go system that follows experts way of thinking and
playing. Our system consists of two parts. The first part is a novel deep
alternative neural network (DANN) used to generate candidates of next move.
Compared with existing deep convolutional neural network (DCNN), DANN inserts
recurrent layer after each convolutional layer and stacks them in an
alternative manner. We show such setting can preserve more contexts of local
features and its evolutions which are beneficial for move prediction. The
second part is a long-term evaluation (LTE) module used to provide a reliable
evaluation of candidates rather than a single probability from move predictor.
This is consistent with human experts nature of playing since they can foresee
tens of steps to give an accurate estimation of candidates. In our system, for
each candidate, LTE calculates a cumulative reward after several future
interactions when local variations are settled. Combining criteria from the two
parts, our system determines the optimal choice of next move. For more
comprehensive experiments, we introduce a new professional Go dataset (PGD),
consisting of 253233 professional records. Experiments on GoGoD and PGD
datasets show the DANN can substantially improve performance of move prediction
over pure DCNN. When combining LTE, our system outperforms most relevant
approaches and open engines based on MCTS.
