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Graph."],"published-print":{"date-parts":[[2014,7,27]]},"abstract":"<jats:p>\n            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\n            <jats:italic>self-refining game<\/jats:italic>\n            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.\n          <\/jats:p>","DOI":"10.1145\/2601097.2601196","type":"journal-article","created":{"date-parts":[[2014,7,22]],"date-time":"2014-07-22T15:08:20Z","timestamp":1406041700000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Self-refining games using player analytics"],"prefix":"10.1145","volume":"33","author":[{"given":"Matt","family":"Stanton","sequence":"first","affiliation":[{"name":"Carnegie Mellon University"}]},{"given":"Ben","family":"Humberston","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}]},{"given":"Brandon","family":"Kase","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}]},{"given":"James F.","family":"O'Brien","sequence":"additional","affiliation":[{"name":"University of California at Berkeley"}]},{"given":"Kayvon","family":"Fatahalian","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}]},{"given":"Adrien","family":"Treuille","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}]}],"member":"320","published-online":{"date-parts":[[2014,7,27]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1073204.1073300"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409060.1409116"},{"volume-title":"Proceedings of the 2010 ACM SIGGRAPH\/Eurographics Symposium on Computer Animation, SCA '10, 197--206","author":"Chentanez N.","key":"e_1_2_2_3_1"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2010324.1964977"},{"key":"e_1_2_2_5_1","unstructured":"Cinematronics 1983. 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