Authorial Control of Monte Carlo Tree Search Agents in Games

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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.