Multiplayer Online Battle Arena (MOBA) games have received increasing popularity recently. In a match of such games, players compete in two teams of five, each controlling an in-game avatars, known as heroes, selected from a roster of more than 100. The selection of heroes, also known as pick or draft, takes place before the match starts and alternates between the two teams until each player has selected one hero. Heroes are designed with different strengths and weaknesses to promote team cooperation in a game. Intuitively, heroes in a strong team should complement each other's strengths and suppressing those of opponents. Hero drafting is therefore a challenging problem due to the complex hero-to-hero relationships to consider. In this paper, we propose a novel hero recommendation system that suggests heroes to add to an existing team while maximizing the team's prospect for victory. To that end, we model the drafting between two teams as a combinatorial game and use Monte Carlo Tree Search (MCTS) for estimating the values of hero combinations. Our empirical evaluation shows that hero teams drafted by our recommendation algorithm have significantly higher win rate against teams constructed by other baseline and state-of-the-art strategies.

本文提出了一种新的英雄推荐系统，通过模拟两个团队之间的“英雄选择”为组合游戏，并使用 Monte Carlo 树搜索来估计英雄组合的价值，从而最大化团队获胜的可能性。实证结果表明，通过我们的推荐算法构建的英雄团队与基准算法和现有技术策略构建的团队相比具有显着更高的胜率。

制定艺术：面向团队的多人在线战斗竞技游戏英雄推荐系统