西洋棋趨勢視覺化技術

Abstract

We present a chess visualization to convey the change in a game over successive generations. It contains a score chart, an evolution graph, and a chess board such that users can understand the game from global viewpoints to local viewpoints. Unlike current graphical chess tools, which focus on only highlighting pieces that are under attacked and require sequential investigation, our visualization shows potential outcomes after a piece is moved and indicates how much tactical advantage the player can take from the opponent. Users can first glance at the score chart to roughly obtain the growth and decline of strengths from both sides, followed by examining the position relationships and the piece placements, to know how the pieces are controlled and how the strategy works. To achieve this visualization, we compute the decision tree using artificial intelligence to analyze the game, in which each node represents a chess position and each edge connects two chess positions that are one-move different. We then merge nodes representing the same chess position and shorten branches less correlated to the main trunk, which represent players' moves, to achieve readability and aesthetics. During the graph rendering, the nodes containing events such as draws, checks, and checkmates are highlighted because they show how a game is ended. As a result, our visualization helps players understand a chess game so that they can learn strategies and tactics efficiently. The presented results and the conducted user studies demonstrate the feasibility of our visualization design.

We present a chess visualization to convey the change in a game over successive generations. It contains a score chart, an evolution graph, and a chess board such that users can understand the game from global viewpoints to local viewpoints. Unlike current graphical chess tools, which focus on only highlighting pieces that are under attacked and require sequential investigation, our visualization shows potential outcomes after a piece is moved and indicates how much tactical advantage the player can take from the opponent. Users can first glance at the score chart to roughly obtain the growth and decline of strengths from both sides, followed by examining the position relationships and the piece placements, to know how the pieces are controlled and how the strategy works. To achieve this visualization, we compute the decision tree using artificial intelligence to analyze the game, in which each node represents a chess position and each edge connects two chess positions that are one-move different. We then merge nodes representing the same chess position and shorten branches less correlated to the main trunk, which represent players' moves, to achieve readability and aesthetics. During the graph rendering, the nodes containing events such as draws, checks, and checkmates are highlighted because they show how a game is ended. As a result, our visualization helps players understand a chess game so that they can learn strategies and tactics efficiently. The presented results and the conducted user studies demonstrate the feasibility of our visualization design.