Combining final score with winning percentage by sigmoid function in Monte-Carlo simulations

Monte-Carlo method recently has produced good results in Go. Monte-Carlo Go uses a move which has the highest mean value of either winning percentage or final score. In a past research, winning percentage is superior to final score in Monte-Carlo Go. We investigated them in BlokusDuo, which is a relatively new game, and showed that Monte-Carlo using final score is superior to the one that uses winning percentage in cases where many random simulations are used. Besides, we showed that using final score is unfavorable for UCT, which is the most famous algorithm in Monte-Carlo Go. To introduce the effectivity of final score to UCT, we suggested a way to combine winning percentage and final score by using sigmoid function. We show the effectivity of the suggested method and show that the method improves a bias where Monte-Carlo Go plays very safe moves when it has advantage.