Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Gameplay Analysis through State Projection Erik Andersen1, Yun-En Liu1, Ethan Apter1, François Boucher-Genesse2, Zoran Popović1 1 Center for Game Science Department of Computer Science University of Washington FDG 2010 2 Department of Education Université du Québec à Montréal June 21st, 2010 We want to know how people play We want to know how people play We want to know how people play ? We want to find… We want to find… • Player confusion We want to find… • Player confusion • Player strategies We want to find… • Player confusion • Player strategies • Design flaws Patterns in data SELECT * FROM replays WHERE location=x AND time>y AND attempt>3 AND death=“grenade”… Patterns in data SELECT * FROM replays WHERE location=x AND time>y AND attempt>3 AND death=“grenade”… Confusion? Strategies? Traditional Playtesting Statistical Methods • Surveys • In-game statistics Statistical Methods • Surveys • In-game statistics Visual Data Mining Lets people see patterns in data Bungie (Halo 3) Visual Data Mining Lets people see patterns in data • Dynamic information? Bungie (Halo 3) Visual Data Mining Lets people see patterns in data • Dynamic information? • Games with no map? Bungie (Halo 3) But what about? But what about? But what about? But what about? “Playtraces” Start Goal “Playtraces” Start Goal “Playtraces” Start Goal “Playtraces” Start Confusion? Distance to goal Goal Refraction Refraction • Massive educational data mining Classic Multidimensional Scaling • 2-D projection of points in high-dimensional space • Clusters game states based on some distance function State Distance State Distance State Distance State Distance Action Distance da (s1, s2) State Distance Start Confusion? Distance to goal Goal Distance to Goal dg (s1, s2) = abs(dg (s1, sg) - dg (s2, sg)) Distance Functions Action distance Distance to goal Combined Refraction Distance Function d (s1, s2) = (da (s1, s2) + dg (s1, s2)) / 2 Playtracer Framework Easy level Difficult level Failure Chance To Win Chance To Win Evaluation Evaluation • 35 children from K12 Virtual Academies Evaluation • 35 children from K12 Virtual Academies • Mostly third and fourth-graders Evaluation • 35 children from K12 Virtual Academies • Mostly third and fourth-graders • About 15 levels Evaluation • 35 children from K12 Virtual Academies • Mostly third and fourth-graders • About 15 levels • The game logged all player actions Analysis Analysis • Player confusion Analysis • Player confusion • Player hypotheses Analysis • Player confusion • Player hypotheses • Design flaws Analysis • Player confusion • Player hypotheses • Design flaws Level 2 Level 2 Solution Level 2 Solution Level 2 Visualization Level 2 Visualization Confusion: Hitting target from wrong side Refinement Refinement Confusion: Using pieces incorrectly Confusion: Using pieces incorrectly Confusion: Using pieces incorrectly Confusion: Using pieces incorrectly Analysis • Player confusion • Player hypotheses • Design flaw Level 4 Level 4 Solution Level 4 Visualization Level 4 Visualization Level 4 Visualization Hypothesis: Satisfy bottom target Hypothesis: Get laser near targets Hypothesis: Overload bottom target Analysis • Player confusion • Player hypotheses • Design flaws Level 4 Visualization Level 4 Visualization Design flaw: Deadly state Refinement Limitations Difficult to find good distance function Limitations Difficult to find good distance function Limitations Difficult to find good distance function Limitations Large game spaces Conclusions • Useful for game analysis Conclusions • Useful for game analysis • We are expanding and refining Playtracer Big Open Problems How to Big Open Problems How to – specify distances between game states Big Open Problems How to – specify distances between game states – differentiate types of confusion Big Open Problems How to – specify distances between game states – differentiate types of confusion – classify player strategies Acknowledgements Marianne Lee Emma Lynch Justin Irwen Happy Dong Brian Britigan Dennis Doan François Boucher-Genesse Seth Cooper Taylor Martin John Bransford David Niemi Ellen Clark Funding: NSF Graduate Fellowship, NSF, DARPA, Adobe, Intel, Microsoft Cycles Acyclic Paths Player Tracking