Modelling Player Behaviour in Total-War Games

在游戏中设计具有挑战性和乐趣的敌人是一项艰巨的任务，特别是由于现代游戏的复杂性越来越高。虽然机器学习是解决这个问题的一个很有前途的解决方案，但大多数关于学习游戏代理的研究都集中在赢得游戏上。此外，专注于学习乐趣代理的少数尝试通常依赖于对乐趣的单一定义，这是有问题的，因为乐趣是什么的主观性质。因此，创建乐趣代理的潜在解决方案是建模人类行为以捕获乐趣的各种定义。因此，我们建议通过复制其内在动机来模拟人类行为。我们认为，一个球员的动机反映在他的打法，因此可以衡量。有了衡量内在动机的指标，我们可以训练类人智能体，优化游戏设计，或者实时调整游戏以适应玩家的动机。

Designing enemies in games that are challenging and fun to play against is a difficult task, especially due to the increasing complexity of modern games. While machine learning is a promising solution to this problem, most research on learning game-playing agents focuses on winning the game. Furthermore, the few attempts that focus on learning fun agents often rely on a single definition of fun, which is problematic due to the subjective nature of what fun is.As such, a potential solution for creating fun agents is modelling human behaviour to capture the varied definitions of fun. Therefore, we propose to model human behaviour by replicating their intrinsic motivation. We argue that a player's motivation is reflected in his playstyle and can thus be measured. With metrics for measuring intrinsic motivation, we can train human-like agents, optimize a game's design, or adapt a game to a player's motivation in real-time.