player co-modelling
Player co-modelling in a strategy board game: discovering how to play fast
In this paper we experiment with a 2-player strategy board game where playing models are evolved using reinforcement learning and neural networks. The models are evolved 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.
cs/0611164
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- North America > United States > Nevada > Clark County > Las Vegas (0.04)
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
- North America > United States > District of Columbia > Washington (0.04)
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