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TactiVis: Towards Better Understanding of Team-Based Combat Tactics.

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    TactiVis visualizes complex combat tactics in team-based games. This system helps analysts understand player decision-making and improve combat strategies through macro-to-micro analysis.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Game Studies

    Background:

    • Team-based combat scenarios, common in video games, generate complex decision-making data.
    • Analyzing these tactics is crucial for understanding game dynamics and enhancing player performance.
    • Existing visualization methods struggle with the multifaceted nature of combat data.

    Purpose of the Study:

    • To introduce TactiVis, a novel visual analytics system for analyzing combat decision-making behavior.
    • To address the challenges in visualizing and analyzing complex, multi-characteristic combat tactics.
    • To provide a framework for understanding combat patterns from match-level to character-level.

    Main Methods:

    • Developed TactiVis, a visual analytics system using a macro-to-micro framework.
    • Employed a three-stage analysis: match-level, event-level, and character-level.
    • Introduced the v-storyline visualization to reveal tactical patterns by encoding vertical positions.

    Main Results:

    • Demonstrated the utility of TactiVis through case studies.
    • Validated the usability of TactiVis in a dedicated study.
    • Showcased the system's effectiveness in helping analysts understand combat patterns and tactics.

    Conclusions:

    • TactiVis offers a powerful solution for analyzing complex combat decision-making data.
    • The system enhances analysts' ability to understand and interpret tactical behaviors in team-based games.
    • The v-storyline visualization is a key innovation for revealing tactical patterns.