Dynamic Game Difficulty Scaling Using Adaptive Behavior-Based AI

Games are played by a wide variety of audiences. Different individuals will play with different gaming styles and employ different strategic approaches. This often involves interacting with nonplayer characters that are controlled by the game AI. From a developer's standpoint, it is important to design a game AI that is able to satisfy the variety of players that will interact with the game. Thus, an adaptive game AI that can scale the difficulty of the game according to the proficiency of the player has greater potential to customize a personalized and entertaining game experience compared to a static game AI. In particular, dynamic game difficulty scaling refers to the use of an adaptive game AI that performs game adaptations in real time during the game session. This paper presents two adaptive algorithms that use ideas from reinforcement learning and evolutionary computation to improve player satisfaction by scaling the difficulty of the game AI while the game is being played. The effects of varying the learning and mutation rates are examined and a general rule of thumb for the parameters is proposed. The proposed algorithms are demonstrated to be capable of matching its opponents in terms of mean scores and winning percentages. Both algorithms are able to generalize well to a variety of opponents.