Player co-modelling in a strategy board game: discovering how to play fast

In this article we experiment with a 2-player strategy board game where playing models are developed using reinforcement learning and neural networks. The models are developed to speed up automatic game development based on human involvement at varying levels of sophistication and density when compared to fully autonomous playing. The experimental results suggest a clear and measurable association between the ability to win games and the ability to do that fast, while at the same time demonstrating that there is a minimum level of human involvement beyond which no learning really occurs.