Authorial Control of Monte Carlo Tree Search Agents in Games

随着人工智能（AI）的发展，数字游戏代理的开发越来越受到学术界的关注。由于策略博弈具有博弈状态空间和动作空间的复杂性，智能代理的实现一直是一个具有挑战性的课题。搜索算法，如蒙特卡罗树搜索（MCTS），在现有的工作中常用来设计代理。然而，其复杂性等实际问题阻碍了其应用，并带来了开放性的研究问题。拟议的研究将侧重于改善MCTS。我们研究了如何确定MCTS的收敛性，如何衡量智能体的决策以及如何建立其意识以实现作者控制。如果研究成功完成，将对学术界和游戏行业产生重大影响，推动进一步的研究方向。

Developing agents for digital games has been receiving increasing attention from academia with the advance of Artificial Intelligence (AI). As the strategy game is complex in terms of the game state space and action space, implementing intelligent agents has been regarded as a challenging topic.Search algorithms, e.g. Monte Carlo Tree Search (MCTS), are commonly used in existing works to design agents. However, practical issues, such as the high complexity, hinder its application and introduce open research problems. The proposed study will focus on improving MCTS. We investigate how to determine the convergence of the MCTS, how to measure agents' decisions and how to build its awareness to realize authorial control. On successful completion, the research will have a great impact on both the academia and game industry, motivating further research directions.