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ShadyStats

ShadyStats is a visualization solution for AI development in the video game Super Mario Strikers. It allows developers to analyze multidimensional statistics, show trends and correlations, find bugs, and tune the game for balanced gameplay. The solution includes features like hierarchical parallel coordinates, dynamic filtering, zoomable graphs, and interactive tooltips.

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ShadyStats

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  1. ShadyStats Final Report Mike Cora December 19, 2005 533C: Information Visualization

  2. Background and Motivation • Spent the last year developing the high-level AI for Super Mario Strikers (decisions and positioning) • Coding, tuning and testing a video game’s AI involved gathering a lot of multidimensional statistics • Task goals for a visualization solution: • Show trends, and correlations, compare teams and difficulty levels • Help with finding bugs during development • Help with tuning a well balanced game • Aid communication with the publisher (across potential language barriers for example)

  3. Proposal: ShadyStats 2) Hierarchical parallel coordinates 1) Dataset history 4 – Dynamic filtering 3 – Zoomable, detailed graphs

  4. Feature Goals • Maintain history of datasets for easy comparison during development lifecycle • Use XmdvTool1 for parallel coordinates: • Hierarchical aggregation, with fancy shading • Extend with interactive drag reordering of dimensions • Generate detailed graphs on demand, using Zedgraph2, by interacting with the parallel coordinates component

  5. Feature Goals 2 • In depth, dynamic filtering and highlighting options • Various statistical measures (mean, variance) • Highly interactive, tooltip information everywhere (data values, dimension names, etc). • Contextual zooming of individual graphs

  6. Demo: Parallel Coordinates • Automatic PCA clustering not applicable, needed direct control over clustering. • Hiding, reordering dimensions. Should be direct interaction with parallel coordinates control, rather than clunky button interface. • Add Side cluster, compare by dataset, difficulty, side, all data. • Highlight dataset, difficulty, side hierarchy zoom. • Zooming, panning of parallel coordinates. • ** Highlite PassReceive and Possession time anomalies.

  7. Demo: Parallel Coordinates

  8. Demo: Graphs • Add Shots vs. Goals • Add Fouls vs. Goals vs. Sides and Difficulty (overlaying curves). • Magnify and delete graphs, naïve, quick and dirty layout, possibly use Piccolo • Highlight apparent trends. • Highlight live data linking between all views. • Built-in zooming, panning and saving features of ZedGraph.

  9. Demo: Graphs

  10. Demo: Filtering • Add filter Side=Home, Difficulty=Easy • Highly customizable and/or filter tree. • Everything is linked and updated dynamically. • More complex filters: dirty, high-scoring games, home/away difficulty comparison.

  11. Demo: Filtering

  12. Demo: Data views • All view parameters stored in one structure • Ability to save and switch between arbitrary number of views • Very easy to implement undo/redo system • Data view copy stored for each view manipulation

  13. Unfinished/Future Work • Fix bugs!! • Optimize!! (don’t use doubles, fix possible OpenGL - C# bottleneck). • Undo/Redo system based on save data views • More interactivity with the parallel coordinate control (dragging, selecting, highlighting) • Data tooltips everywhere • Coloring of parallel coordinates more meaningful, rather than automatic • Link colors between parallel coordinates and graphs more.

  14. Unfinished/Future Work • Improve mean line, more statistical analysis • Highlighting of specific games by selecting data points in parallel coord or graphs. • Improve filtering interface, may be a bit cumbersome • Possibly use Piccolo for graph layout • Evaluation: • After the hollidays, our QA Lead will evaluate the ShadyStats, apply it to our datasets, and suggest features and improvements.

  15. Bibliography • XmdvTool: http://davis.wpi.edu/~xmdv/ • Zedgraph: http://zedgraph.sourceforge.net/ • Edward J. Wegman. Hyperdimensional Data Analysis Using Parallel Coordinates, Journal of the American Statistical Association, Vol. 85, No. 411. (Sep., 1990), pp. 664-675. • Ying-Huey Fua, Matthew O. Ward, and Elke A. Rundensteiner, Hierarchical Parallel Coordinates for Visualizing Large Multivariate Data Sets, IEEE Visualization '99. • Jing Yang, Wei Peng, Matthew O. Ward and Elke A. Rundensteiner, Interactive Hierarchical Dimension Ordering, Spacing and Exploration of High Dimensional Datasets, Proc. InfoVis 2003. • John K. Ousterhout, Tcl and the Tk Toolkit, Addison Wesley,1994.

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