Go-Ahead: Improving Prior Knowledge Heuristics by Using Information Retrieved From Play Out Simulations

The proposal behind this paper is the introduction of a new agent denominated Go-Ahead: this is an automatic Go player that uses a new technique in order to improve the accuracy of the pre estimated values of the moves that are candidates to be introduced into the classical Monte Carlo tree search (MCTS) algorithm which is used by many of the current top agents for Go. Go-Ahead is built upon the framework of one of these agents: the well known open source automatic player Fuego, in which these pre estimated values are obtained by means of a heuristic called prior knowledge. Go-Ahead copes with the task of refining the calculations of these values through a new technique that performs a balanced combination between the prior knowledge heuristic and some relevant information retrieved from the numerous play out simulation phases that are repeatedly executed throughout the Monte Carlo search. With such a strategy, Go-Ahead provides the contribution of enhancing the MCTS process of choosing appropriate moves. Further, this new approach attenuates the supervision level inherent to this process due to the following fact: it allows for the lessening of the impact of the prior knowledge heuristics through strengthening the impact of play out information. The results obtained in tournaments against Fuego confirm the benefits and the contributions provided by this approach.