强化学习干预措施对有限的理性人类代理人在摩擦性任务中进行干预
Eura Nofshin1, Siddharth Swaroop1, Weiwei Pan1
1Harvard University, Cambridge, USA.
概括
人工智能 (AI) 代理人可以个性化干预,帮助人们克服行为挑战. 行为模型强化学习 (BMRL) 使人工智能能够快速,可解释地帮助用户实现长期目标.
科学领域:
- 人工智能的人工智能
- 行为科学 行为科学
- 强化学习是一种强化学习.
背景情况:
- 许多关键的行为改变是困难的,需要持续的努力而没有立即的回报.
- 人工智能 (AI) 提供了个性化干预的潜力,以支持目标坚持.
- 人工智能代理需要快速,可解释地个性化干预,以获得有效的行为支持.
研究的目的:
- 为人工智能驱动的行为干预引入行为模型强化学习 (BMRL).
- 在马尔科夫决策过程 (MDP) 中,模型人类决策作为规划代理.
- 将低于最佳的人力政策归因于不适应的MDP参数.
主要方法:
- 开发了一个框架,在这个框架中,人工智能干预了一个有限的理性人类代理人的MDP参数.
- 提出了可处理的人类模型,以捕捉在艰难任务中的行为.
- 引入了针对BMRL的MDP等价性.
主要成果:
- 人工智能计划与拟议的人类模型导致有效的政策.
- 证明了对BMRL框架的理论和经验支持.
- 显示AI可以帮助个人在复杂,现实世界的场景中坚持目标.
结论:
- BMRL为AI提供了一种新的方法,以支持长期的行为变化.
- 可解释的AI干预可以增强对行为动态的理解.
- 该框架有效地解决了需要持续努力的摩擦性任务中的挑战.
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