Improvement of move naturalness for playing good-quality games with middle-level players

In the game field, computer programs have surpassed top human players in many games. A well-known example is AlphaZero. These strong programs provide human players with opportunities to improve their skills. However, human players may not enjoy such strong opponents. To make middle-level players learn from playing good-quality games with strong programs, we have proposed to combine programs with distinct roles in our previous research. One role is a superhuman program that proposes and accurately evaluates candidate moves. The other role is a naturalness (or human likeness) evaluator. Candidate moves are evaluated by combining the two roles using a function, and the moves with the highest scores are played. This study builds upon our earlier work to further improve the naturalness of moves. First, we propose a search mechanism inspired by the sequential halving algorithm to decide candidate moves and the moves to play. Second, we propose a new score function to address several issues of the previous approach. We conduct experiments to compare the proposed approaches with several existing approaches. The results show that the move naturalness of he proposed approaches is greatly improved and that performance in other aspects is at least as good as existing approaches.