Q-Learning with Naive Bayes Approach Towards More Engaging Game Agents

One of the goals of modern game programming is adapting the life-like characteristics and concepts into games. This approach is adopted to offer game agents that exhibit more engaging behavior. Methods that prioritize reward maximization cause the game agent to go into same patterns and lead to repetitive gaming experience, as well as reduced playability. In order to prevent such repetitive patterns, we explore a behavior algorithm based on Q-learning with a Naive Bayes approach. The algorithm is validated in a formal user study in contrast to a benchmark. The results of the study demonstrate that the algorithm outperforms the benchmark and the game agent becomes more engaging as the amount of gameplay data, from which the algorithm learns, increases.