辅助多臂强盗环境中的风险厌恶偏置人类策略
Risk-Averse Biased Human Policies in Assistive Multi-Armed Bandit Settings
摘要：
辅助多臂强盗问题可以用来模拟人类和自主系统（如家庭服务机器人）之间的团队情况。为了解释人类的偏见，如累积前景理论中描述的风险厌恶，设置扩展到使用可观察的奖励。当机器人利用关于风险厌恶的人类模型的知识时，它们消除了偏见，做出了更理性的选择。我们提出了一种算法，增加了这样的人类机器人团队的实用价值。一个简短的评估表明，可以处理任意的奖励函数。
英文摘要：
Assistive multi-armed bandit problems can be used to model team situations between a human and an autonomous system like a domestic service robot. To account for human biases such as the risk-aversion described in the Cumulative Prospect Theory, the setting is expanded to using observable rewards. When robots leverage knowledge about the risk-averse human model they eliminate the bias and make more rational choices. We present an algorithm that increases the utility value of such human-robot teams. A brief evaluation indicates that arbitrary reward functions can be handled.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
