Hero drafting is a challenging problem in MOBA (MultiPlayer Online Battle Arena) games due to the complex hero-to-hero relationships to consider. 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 a higher win rate against teams constructed by other baseline and state-of-the-art strategies.
Members of the Project:
Zhengxing Chen, Truong-Huy D Nguyen, Yuyu Xu, Chris Amato, Seth Cooper, Yizhou Sun, Magy Seif El-Nasr