Enhancing player experience in computer games: A computational Intelligence approach.

Abstract

Gaming is by definition an interactive experience that often involves the human player interacting with the non-player characters in the game which are in turn controlled by the game artificial intelligence. Research in game AI has traditionally been focused on improving its competency. However, a competent game AI does not directly correlate to the satisfaction and entertainment value experienced by the human player. This thesis focuses on addressing two key issues of game AI affecting the player experience, namely adaptability and believability, in real time computer games from a computational intelligence perspective. The nature of real time computer games requires that the game AI be computationally efficient in addition to being competent in the game. This thesis starts off by proposing a hybrid evolutionary behaviour-based design framework that combines the good response time of behaviour-based systems and the search capabilities of evolutionary algorithms. The result is a scalable framework where new behaviours can be easily introduced. This lays the groundwork for investigations into enhancing the player experience. Two adaptive algorithms are built upon the proposed framework to address the issue of adaptability in games. The two proposed adaptive algorithms draw inspirations from reinforcement learning and evolutionary algorithms to dynamically scale the difficulty of the game AI while the game is being played such that offline training is not necessary. Such an adaptive system has the potential to customize a personalized experience that grows together with the human player. The game AI framework is also augmented by the introduction of evolved sensor noise in order to induce game agents with believable movement behaviours. Furthermore, the action histogram and action sequence histogram are explored as a means to quantify the believability of the game agent?s movements. A multi-objective optimization approach is then used to improve the believability of the game agent without degrading its performance and the results are verified in a user study. Improving the believability of game agents has the potential to maintain the suspension of disbelief and increase immersion in the game environment.