Challenge-sensitive action selection: an application to game balancing
Challenge-sensitive action selection: an application to game balancing
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挑战敏感的动作选择：游戏平衡的应用
DOI：
10.1109/iat.2005.52
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
V. Corruble
中科院分区：
文献类型：
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作者：
Gustavo Andrade;Geber Ramalho;Hugo Santana;V. Corruble
处理不同技能的用户，以及随着时间的推移学习和适应能力可变的用户，是人机交互中的一个关键问题，特别是在高度互动的应用程序中，如计算机游戏。实际上，游戏开发者社区的一个公认的主要关注点是提供动态平衡游戏难度级别的机制，以便保持用户对玩游戏的兴趣。这项工作提出了一种创新的使用强化学习技术来构建智能代理，以适应他们的行为，以提供动态的游戏平衡。其想法是将学习与动作选择机制结合起来，该机制取决于对当前用户技能的评估。为了验证我们的方法，我们将其应用到实时格斗游戏中，获得了良好的效果，因为自适应代理能够快速地与具有不同技能的对手在同一水平上比赛。
Dealing with users of different skills, and of variable capacity for learning and adapting over time, is a key issue in human-machine interaction, particularly in highly interactive applications such as computer games. Indeed, a recognized major concern for the game developers' community is to provide mechanisms to dynamically balance the difficulty level of the games in order to keep the user interested in playing. This work presents an innovative use of reinforcement learning techniques to build intelligent agents that adapt their behavior in order to provide dynamic game balancing. The idea is to couple learning with an action selection mechanism which depends on the evaluation of the current user's skills. To validate our approach, we applied it to a real-time fighting game, obtaining good results, as the adaptive agent is able to quickly play at the same level as opponents with different skills.
RoboCup足球小型联赛比赛记者系统的开发
DOI：
--
发表时间：
2021
期刊：
影响因子：
--
作者：
A. Odanaka;T. Sugiura;D. Oikawa;T. Tsukamoto;H. Andoh
通讯作者：
H. Andoh