Combining Old-Fashioned Computer Go with Monte Carlo Go

In this paper we discuss the idea of combining old-fashioned computer Go with Monte Carlo Go. We introduce an analyze-after approach to random simulations. We also briefly present the other features of our present Monte Carlo implementation with upper confidence trees. We then explain our approach to adding this implementation as a module in the GNU Go 3.6 engine, and finally show some preliminary results of the entire work, ideas for future work and conclusions.