Self-Refining Games using Player Analytics

Data-driven simulation demands good training data drawn from a vast space of possible simulations. While fully sampling these large spaces is infeasible, we observe that in practical applications, such as gameplay, users explore only a vanishingly small subset of the dynamical state space. I n this paper we present a sampling ap-proach that takes advantage of this observation by concentrating precomputation around the states that users are most likely to en-counter. We demonstrate our technique in a prototype self -refining game whose dynamics improve with play, ultimately providing re-alistically rendered, rich fluid dynamics in real time on a mobile de-vice. Our results show that our analytics-driven training approach yields lower model error and fewer visual artifacts than a heuristic trainin g strategy.