玩广泛的游戏,学习对手的认知能力
Chanjuan Liu1, Jinmiao Cong1, Weihong Yao1
1School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
本研究介绍了对抗性认知平衡 (ACE),这是一个新的游戏理论概念,通过考虑对手的认知来增强代理决策. 通过结合对手建模,ACE提供了一种比认知完美平衡 (CPE) 更现实的方法.
科学领域:
- 人工智能的人工智能
- 游戏理论 游戏理论
- 计算经济学计算经济学
背景情况:
- 智能代理人的决策至关重要,认知影响结果.
- 广泛的游戏模拟交互式决策场景.
- 像认知完美平衡 (CPE) 这样的先前模型包含了游戏结构的代理认知,但不是对手认知.
研究的目的:
- 为广泛游戏提出一个新的框架,它结合了代理人对对手的认知.
- 引入一种新的平衡概念,即对抗性认知平衡 (ACE),可以解释对手建模.
- 为了证明与CPE相比,ACE的增强现实主义和战略影响.
主要方法:
- 在扩展游戏中开发了一个评估对手认知能力的框架.
- 设计和分析了一种用于玩游戏的算法,其中包含对手认知.
- 介绍了对抗性认知平衡 (ACE) 概念.
主要成果:
- 通过包括对手的认知学习,ACE被证明比CPE更现实.
- 通过运行示例证明了ACE框架的有效性.
- 介绍了关于ACE计算复杂性,稳定性,完整性和存在的发现.
结论:
- 拟议的框架通过评估对手的认知来提高代理人的战略能力.
- ACE代表了一种更复杂,更现实的方法来建模交互式决策.
- 这项工作为开发能够进行复杂战略互动的先进AI代理开辟了新的途径.
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