  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.
