闭环不确定性:在数据漂移下的人机团队的不确定性评估和校准
Zachary Bishof1, Jaelle Scheuerman1, Chris J Michael1
1U.S. Naval Research Laboratory, 1005 Balch Boulevard, Stennis Space Center, St. Louis, MS 39529, USA.
Entropy (Basel, Switzerland)
|October 28, 2023
概括
本研究引入了一个闭环不确定性框架,以代评估机器正确性概率. 这种新的方法通过结合反循环,显著改善了人机团队的不确定性建模.
科学领域:
- 人机系统 人机系统
- 机器学习 机器学习
- 决策科学 决策科学 决策科学
背景情况:
- 准确的不确定性测量对于人机团队的成功至关重要.
- 当前的方法经常聚合评估,未能捕捉代控制过程.
- 在没有立即反的情况下,在冷启动或数据漂移期间,透指标可能会产生影响.
研究的目的:
- 提出一个随机框架,以代评估不确定性模型作为机器正确性的概率.
- 为了解决值选择问题,一个新的主观用户任务用于实验.
- 探索将机器正确性反纳入基线模型,使用强化学习.
主要方法:
- 开发了一种用于代不确定性模型评估的随机框架.
- 介绍了人机实验的值选择问题.
- 实施了强化学习方法来完善一个纯粹的贝叶斯不确定性模型,并提供正确性反.
主要成果:
- 新的闭环不确定性方法被反复测试.
- 实验表明与基线模型相比,表现始终优于基线模型.
- 在不确定性评估方面,平均得到了大约45%的改进.
结论:
- 机器正确性的代反显著提高了不确定性建模.
- 闭环不确定性框架提供了一个强大的方法来提高人机团队的表现.
- 这种方法提供了更准确的机器正确性概率,特别是在动态环境中.
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