Extending Real-Time Challenge Balancing to Multiplayer Games: A Study on Eco-Driving

Q2 Computer Science

IEEE Transactions on Computational Intelligence and AI in Games Pub Date : 2016-03-01 DOI:10.1109/TCIAIG.2014.2364258

H. Prendinger, Kamthorn Puntumapon, Marconi Madruga Filho

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引用次数: 5

Abstract

Multiplayer games are an important and popular game mode for networked players. Since games are played by a diverse audience, it is important to scale the difficulty, or challenge, according to the skill level of the players. However, current approaches to real-time challenge balancing (RCB) in games are only applicable to single-player scenarios. In multiplayer scenarios, players with different skill levels may be present in the same area, and hence adjusting the game difficulty to match the skill of one player may affect the other players in an undesirable way. To address this problem, we have previously developed a new approach based on distributed constraint optimization, which achieves the optimal challenge level for multiple players in real-time. The main contribution of this paper is an experiment that was performed with our new multiplayer real-time challenge balancing method applied to eco-driving. The results of the experiment suggest the effectiveness of RCB.

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将实时挑战平衡扩展到多人游戏:关于生态驾驶的研究

多人游戏是网络玩家的一种重要而流行的游戏模式。因为玩游戏的是各种各样的用户，所以根据玩家的技能水平来调整难度或挑战是很重要的。然而，当前游戏中的实时挑战平衡(RCB)方法只适用于单人游戏场景。在多人游戏场景中，不同技能水平的玩家可能出现在同一区域，因此调整游戏难度以匹配一个玩家的技能可能会以一种不受欢迎的方式影响到其他玩家。为了解决这个问题，我们之前开发了一种基于分布式约束优化的新方法，该方法可以实时实现多个玩家的最佳挑战级别。本文的主要贡献是将我们的新多人实时挑战平衡方法应用于生态驾驶的实验。实验结果表明了RCB的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

4.60

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.