个人和团队分析,以支持人工社会智能的思想理论
Rhyse Bendell1,2, Jessica Williams3,4, Stephen M Fiore5,6
1Team Performance Laboratory, Institute for Simulation and Training, University of Central Florida, Orlando, FL, 32816, USA. rhyse.bendell@ucf.edu.
Scientific reports
|June 2, 2024
概括
人工智能可以通过使用关于人类队友的可量化的数据来开发思想理论. 这种方法增强了团队的协作,提高了绩效,特别是那些使用人工社会智能 (ASI) 工作的潜力较低的团队.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 认知科学 认知科学
背景情况:
- 开发具有社会认知能力的人工智能对于有效的人类-AI合作至关重要.
- 当前的人工智能系统往往缺乏理解人类队友心理状态的能力,阻碍了团队的表现.
- 人工智能中的思维理论 (ToM) 对于预测和响应团队环境中的人类行为至关重要.
研究的目的:
- 提出一种新的方法,使人工智能能够开发关于人类队友的思维理论 (ToM).
- 调查如何量化,机器可读的关于人类团队成员的信息可以支持AI的ToM开发.
- 评估人工智能驱动的ToM对团队在复杂协调任务中的表现和感知的影响.
主要方法:
- 开发了一种分析方法,从稀疏的数据中构建个人和团队特征资料.
- 收集可量化的先验信息,关于人工智能代理人的团队成员.
- 进行了一项实验,特设团队与人工社会智能 (ASI) 队友一起执行复杂的协调任务.
- 分析了个人/团队配置文件,任务性能和用户对ASI的看法之间的关系.
主要成果:
- 个人和新兴的团队配置文件有效地描述了团队特征,预测行为并解释了对ASI的看法.
- ASI顾问在任务成果指标中显著提高了低潜力团队的表现.
- 在与人类与ASI顾问互动时,团队个人资料对绩效的影响可能会有所不同.
结论:
- 提供可量化的人类特征数据使人工智能能够开发ToM,增强团队互动.
- 像ASI这样具有社会认知能力的AI可以显著提高团队的有效性,特别是在表现不佳的团队中.
- 这项研究有助于开发智能技术,参与协作行为,以改善团队成果.
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