Hero drafting is essential in MOBA game playing as it builds the team of each
side and directly affects the match outcome. State-of-the-art drafting methods
fail to consider: 1) drafting efficiency when the hero pool is expanded; 2) the
multi-round nature of a MOBA 5v5 match series, i.e., two teams play best-of-N
and the same hero is only allowed to be drafted once throughout the series. In
this paper, we formulate the drafting process as a multi-round combinatorial
game and propose a novel drafting algorithm based on neural networks and
Monte-Carlo tree search, named JueWuDraft. Specifically, we design a long-term
value estimation mechanism to handle the best-of-N drafting case. Taking Honor
of Kings, one of the most popular MOBA games at present, as a running case, we
demonstrate the practicality and effectiveness of JueWuDraft when compared to
state-of-the-art drafting methods.

本研究将策略和组队配合的重要性引入 MOBA 游戏英雄招选中，提出了一种新的基于神经网络和蒙特卡罗树搜索算法的招选算法，名为 JueWuDraft，可以更高效的完成英雄招选，使得招选英雄的效率得到了提升。

如何选择英雄？神经网络和搜索树帮助你学会在 MOBA 游戏中选人

Which Heroes to Pick? Learning to Draft in MOBA Games with Neural  Networks and Tree Search

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。