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人类对自适应机器的适应趋于游戏理论平衡
Benjamin J Chasnov1, Lillian J Ratliff1, Samuel A Burden2
1Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, 98195, USA.
Scientific reports
|August 12, 2025
概括
游戏理论中的机器学习算法可以预测和控制人机交互. 一个算法令人惊地将交互指向机器.
科学领域:
- 人工智能的人工智能
- 游戏理论 游戏理论
- 人与计算机的交互
背景情况:
- 人与机器之间的协同适应性相互作用是复杂的.
- 传统方法通常需要估计人类的实用功能,这是计算密集的.
- 了解人机系统中新出现的行为至关重要.
研究的目的:
- 评估机器学习算法,以控制与人类参与的总和游戏中的结果.
- 调查机器是否可以在没有明确的实用函数估计的情况下影响人类行为.
- 探索游戏理论在设计人机交互结果中的应用.
主要方法:
- 在总和游戏中测试三个不同的机器学习算法.
- 直接利用观察到的人类行为,绕过反向效用函数估计.
- 在行动和政策空间中分析游戏理论平衡的选择.
主要成果:
- 一个算法展示了将互动引导到机器的最佳结果的能力.
- 机器成功地控制了人类的行为,即使人类对他们的感知成本做出了最佳反应.
- 算法通过从游戏理论平衡中进行选择,有效地管理了协同适应的交互.
结论:
- 机器学习算法可以有效地预测和设计人机协同适应系统中的结果.
- 游戏理论为控制复杂的人机环境中的交互提供了一个框架.
- 从人类行为直接学习提供了一个比估计实用函数更有效的方法.
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