A Pattern Mining Approach to Study Strategy Balance in RTS Games
A Pattern Mining Approach to Study Strategy Balance in RTS Games
复制标题
研究RTS游戏策略平衡的模式挖掘方法
DOI：
10.1109/tciaig.2015.2511819
复制
发表时间：
2017
影响因子：
--
通讯作者：
Mehdi Kaytoue
中科院分区：
文献类型：
--
作者：
G. Bosc;Philip Tan;Jean;Chedy Raïssi;Mehdi Kaytoue
虽然像围棋和国际象棋这样纯粹的战略游戏似乎是永恒的，但视频游戏的寿命很短，受到流行文化，趋势，无聊和技术创新的影响。即使是编辑分配的重要预算和开发也不能保证永恒的成功。相反，新奇和更正建议延长不可避免的有限寿命。新奇的东西可能会出乎意料地打破游戏的平衡，因为玩家可能会发现开发人员没有考虑到的不平衡策略。在电子竞技的新背景下，一个重要的挑战是能够检测游戏平衡问题。在本文中，我们认为实时策略（RTS）游戏，并提出了一个有效的模式挖掘算法作为游戏平衡设计师的基本工具，使一个搜索历史数据中的不平衡策略，通过知识发现数据库（KDD）的过程。我们用我们的算法在星际争霸II的历史数据上进行实验，作为一项电子运动进行专业比赛。
Whereas purest strategic games such as Go and Chess seem timeless, the lifetime of a video game is short, influenced by popular culture, trends, boredom, and technological innovations. Even the important budget and developments allocated by editors cannot guarantee a timeless success. Instead, novelties and corrections are proposed to extend an inevitably bounded lifetime. Novelties can unexpectedly break the balance of a game, as players can discover unbalanced strategies that developers did not take into account. In the new context of electronic sports, an important challenge is to be able to detect game balance issues. In this paper, we consider real-time strategy (RTS) games and present an efficient pattern mining algorithm as a basic tool for game balance designers that enables one to search for unbalanced strategies in historical data through a knowledge discovery in databases (KDD) process. We experiment with our algorithm on StarCraft II historical data, played professionally as an electronic sport.