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. In this paper we present a sampling approach that takes advantage of this observation by concentrating precomputation around the states that users are most likely to encounter. We demonstrate our technique in a prototype self-refining game whose dynamics improve with play, ultimately providing realistically rendered, rich fluid dynamics in real time on a mobile device. Our results show that our analytics-driven training approach yields lower model error and fewer visual artifacts than a heuristic training strategy.
Matt Stanton, Ben Humberston, Brandon Kase, James F. O'Brien, Kayvon Fatahalian, and Adrien Treuille. "Self-Refining Games using Player Analytics". ACM Transactions on Graphics, 33(4):xx:1–9, July 2014. To be presented at SIGGRAPH 2014, Vancouver.