Quantifying Gaming Strategy Diversity Through Game Play Trajectory

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题名:	Quantifying Gaming Strategy Diversity Through Game Play Trajectory
作者:	陳弈學;Chen, Yi-Xue
贡献者:	資訊工程學系
关键词:	演算法
日期:	2025-07-17
上传时间:	2025-10-17 12:33:10 (UTC+8)
出版者:	國立中央大學
摘要:	在現代遊戲設計中，策略多樣性——即玩家能以多種可行策略通關——是衡量遊戲設計品質的重要指標，能提升創造力、參與感與重玩性。傳統上，這類評估多依賴人類測試員反覆遊玩，但此方法不僅成本高、耗時，且易受主觀偏誤影響。隨著強化學習的發展，具備自主探索能力與行為多樣性的智能體已被引入遊戲測試領域。然而，雖然相關研究大量著墨於提升智能體的行為多樣性，如何從這些行為中分析遊戲本身的策略多樣性仍是一項未被充分探討的議題。本研究提出一套分析框架，利用智能體探索所產生的行為軌跡來評估遊戲中的策略多樣性。我們的方法從軌跡中擷取關鍵決策狀態，並將問題轉化為圖論模型，透過多樣性近似最短路徑演算法於狀態轉移圖中找出多條具策略差異性的通關路徑。此方法可自動且量化地分析遊戲的策略彈性，無需人工規則或回饋，即可提供客觀的設計品質評估依據。 ;In modern game design, strategic diversity—the ability for players to complete a game through multiple viable strategies—is a key indicator of design quality, contributing to creativity, engagement, and replayability. Traditional evaluation methods rely on human testers, which are costly, time-consuming, and prone to subjective bias. Recent advances in reinforcement learning have introduced agents capable of autonomously exploring game environments and exhibiting diverse behaviors. However, while much attention has been devoted to increasing agent diversity, the problem of analyzing the diversity of the game environment itself remains underexplored. In this work, we propose a framework to assess gameplay diversity by analyzing agent-generated behavior trajectories. Our approach identifies key decision states from the agent’s exploration and formulates a graph-based problem to compute diverse paths toward the goal. By applying diverse approximate shortest path algorithms on a state-transition graph, we uncover multiple distinct solutions within the game. This provides a quantitative, automated means to evaluate the strategic flexibility of a game, offering objective insights into its design quality without relying on handcrafted rules or human feedback.
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