gameplay analysis through state projection erik andersen 1, yun-en liu 1, ethan apter 1, françois...
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- Gameplay Analysis through State Projection Erik Andersen 1, Yun-En Liu 1, Ethan Apter 1, Franois Boucher-Genesse 2, Zoran Popovi 1 1 Center for Game Science Department of Computer Science University of Washington 2 Department of Education Universit du Qubec Montral FDG 2010 June 21 st, 2010
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- We want to know how people play
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- ?
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- We want to find
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- Player confusion
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- We want to find Player confusion Player strategies
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- We want to find Player confusion Player strategies Design flaws
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- Patterns in data SELECT * FROM replays WHERE location=x AND time>y AND attempt>3 AND death=grenade
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- Patterns in data SELECT * FROM replays WHERE location=x AND time>y AND attempt>3 AND death=grenade Confusion? Strategies?
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- Traditional Playtesting
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- Statistical Methods Surveys In-game statistics
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- Statistical Methods Surveys In-game statistics
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- Visual Data Mining Lets people see patterns in data Bungie (Halo 3)
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- Visual Data Mining Lets people see patterns in data Dynamic information? Bungie (Halo 3)
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- Visual Data Mining Lets people see patterns in data Dynamic information? Games with no map? Bungie (Halo 3)
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- But what about?
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- Playtraces GoalStart
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- Playtraces GoalStart
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- Playtraces GoalStart
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- Playtraces GoalStart Confusion? Distance to goal
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- Refraction
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- Massive educational data mining
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- 2-D projection of points in high-dimensional space Clusters game states based on some distance function Classic Multidimensional Scaling
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- State Distance
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- Action Distance d a (s 1, s 2 )
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- State Distance GoalStart Confusion? Distance to goal
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- Distance to Goal d g (s 1, s 2 ) = abs(d g (s 1, s g ) - d g (s 2, s g ))
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- Distance Functions Action distanceCombinedDistance to goal
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- Refraction Distance Function d (s 1, s 2 ) = (d a (s 1, s 2 ) + d g (s 1, s 2 )) / 2
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- Playtracer Framework
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- Easy level
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- Difficult level
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- Failure
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- Chance To Win
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- Evaluation
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- 35 children from K12 Virtual Academies
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- Evaluation 35 children from K12 Virtual Academies Mostly third and fourth-graders
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- Evaluation 35 children from K12 Virtual Academies Mostly third and fourth-graders About 15 levels
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- Evaluation 35 children from K12 Virtual Academies Mostly third and fourth-graders About 15 levels The game logged all player actions
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- Analysis
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- Player confusion
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- Analysis Player confusion Player hypotheses
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- Analysis Player confusion Player hypotheses Design flaws
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- Analysis Player confusion Player hypotheses Design flaws
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- Level 2
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- Level 2 Solution
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- Level 2 Visualization
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- Confusion: Hitting target from wrong side
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- Refinement
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- Confusion: Using pieces incorrectly
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- Analysis Player confusion Player hypotheses Design flaw
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- Level 4
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- Level 4 Solution
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- Level 4 Visualization
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- Hypothesis: Satisfy bottom target
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- Hypothesis: Get laser near targets
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- Hypothesis: Overload bottom target
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- Analysis Player confusion Player hypotheses Design flaws
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- Level 4 Visualization
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- Design flaw: Deadly state
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- Refinement
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- Limitations Difficult to find good distance function
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- Limitations Difficult to find good distance function
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- Limitations Difficult to find good distance function
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- Limitations Large game spaces
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- Conclusions Useful for game analysis
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- Conclusions Useful for game analysis We are expanding and refining Playtracer
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- Big Open Problems How to
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- Big Open Problems How to specify distances between game states
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- Big Open Problems How to specify distances between game states differentiate types of confusion
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- Big Open Problems How to specify distances between game states differentiate types of confusion classify player strategies
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- Acknowledgements Marianne Lee Emma Lynch Justin Irwen Happy Dong Brian Britigan Dennis Doan Franois Boucher-Genesse Seth Cooper Taylor Martin John Bransford David Niemi Ellen Clark Funding: NSF Graduate Fellowship, NSF, DARPA, Adobe, Intel, Microsoft
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- Cycles
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- Acyclic Paths
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- Player Tracking
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