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    Area of Science:

    • Artificial Intelligence
    • Game Theory
    • Data Visualization

    Background:

    • Traditional chess analysis tools often require sequential investigation.
    • Understanding game evolution and tactical advantages is crucial for chess players.
    • Existing visualizations lack a comprehensive overview of game dynamics.

    Purpose of the Study:

    • To develop an AI-powered chess visualization for understanding game changes across generations.
    • To provide users with both global and local perspectives of a chess game.
    • To enhance chess learning by illustrating strategic and tactical elements.

    Main Methods:

    • Utilized artificial intelligence to compute a decision tree representing chess game states.
    • Developed a visualization incorporating a score chart, evolution graph, and chess board.
    • Implemented node merging and branch shortening for improved graph readability.
    • Highlighted significant game events like draws, checks, and checkmates.

    Main Results:

    • The visualization effectively conveys changes in a chess game over successive generations.
    • It allows users to understand piece control, strategic execution, and tactical advantages.
    • The system successfully identifies and highlights critical game events.
    • User studies demonstrated the feasibility and effectiveness of the visualization design.

    Conclusions:

    • The AI-driven chess visualization offers a powerful tool for learning chess strategies and tactics.
    • It provides a more intuitive and comprehensive understanding of game dynamics compared to existing methods.
    • The visualization facilitates efficient learning and strategic analysis for chess players.