在人类和机器学习中自我导向
Julian De Freitas1, Ahmet Kaan Uğuralp2, Zeliha Oğuz-Uğuralp3
1Marketing Unit, Harvard Business School, Boston, MA, USA. jdefreitas@hbs.edu.
Nature human behaviour
|August 31, 2023
概括
人类擅长自我导向,这是一个关键的计算过程,用于在空间和时间中识别自己. 这种对灵活导航至关重要的能力,在简单的自我发现任务中超越了当前的AI算法.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
背景情况:
- 自我的计算概念涉及到一个在特定的时间和地点识别自己身体表征的代理人.
- 这种自我表现是作为自我导向过程的框架,这对人工智能来说是一个重大的计算挑战.
- 自我导向对于代理人有效地与环境互动和导航至关重要.
研究的目的:
- 研究人类代理人的自我导向的计算过程.
- 将人类的自我定向能力与深度强化学习算法的能力进行比较.
- 探索自我导向在实现灵活导航中的作用.
主要方法:
- 使用简单的视频游戏开发"自我发现"任务.
- 招募124名人类玩家在游戏环境中识别自己.
- 对玩家在自我导向任务中的表现进行定量和定性评估.
- 对相同任务的已建立的深度强化学习算法的评估.
主要成果:
- 人类玩家在自我导向任务中表现得近乎最佳.
- 深度强化学习算法的性能明显低于最佳水平.
- 人类和人工智能的自我定向能力之间存在着显著的差异,尽管人工智能在复杂的游戏中取得了成功.
结论:
- 人类拥有非常有效的自我导向机制.
- 目前的深度强化学习方法并没有优化自我导向.
- 自我导向被提议作为一个关键组成部分,以使代理商能够灵活地进行环境导航.
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