Team Performance Laboratory, Institute for Simulation and Training, University of Central Florida, Orlando, FL, 32816, USA.
Department of Psychology, University of Central Florida, Orlando, FL, 32816, USA.
Sci Rep. 2024 Jun 2;14(1):12635. doi: 10.1038/s41598-024-63122-8.
We describe an approach aimed at helping artificial intelligence develop theory of mind of their human teammates to support team interactions. We show how this can be supported through the provision of quantifiable, machine-readable, a priori information about the human team members to an agent. We first show how our profiling approach can capture individual team member characteristic profiles that can be constructed from sparse data and provided to agents to support the development of artificial theory of mind. We then show how it captures features of team composition that may influence team performance. We document this through an experiment examining factors influencing the performance of ad-hoc teams executing a complex team coordination task when paired with an artificial social intelligence (ASI) teammate. We report the relationship between the individual and team characteristics and measures related to task performance and self-reported perceptions of the ASI. The results show that individual and emergent team profiles were able to characterize features of the team that predicted behavior and explain differences in perceptions of ASI. Further, the features of these profiles may interact differently when teams work with human versus ASI advisors. Most strikingly, our analyses showed that ASI advisors had a strong positive impact on low potential teams such that they improved the performance of those teams across mission outcome measures. We discuss these findings in the context of developing intelligent technologies capable of social cognition and engage in collaborative behaviors that improve team effectiveness.
我们描述了一种旨在帮助人工智能培养对其人类队友的心理理论的方法，以支持团队交互。我们展示了如何通过向代理提供关于人类团队成员的可量化、机器可读的先验信息来支持这一点。我们首先展示了我们的分析方法如何能够捕获可以从稀疏数据中构建并提供给代理以支持人工智能心理理论发展的个体团队成员特征配置文件。然后，我们展示了它如何捕获可能影响团队绩效的团队构成特征。我们通过一项实验来证明这一点，该实验考察了在与人工智能社交智能 (ASI) 队友一起执行复杂团队协调任务时，影响临时团队绩效的因素。我们报告了个体和团队特征以及与任务绩效和自我报告的 ASI 感知相关的度量之间的关系。结果表明，个体和新兴的团队概况能够描述预测行为的团队特征，并解释对 ASI 的感知差异。此外，当团队与人类或 ASI 顾问合作时，这些配置文件的特征可能会以不同的方式相互作用。最引人注目的是，我们的分析表明，ASI 顾问对低潜力团队有很强的积极影响，以至于它们提高了这些团队在整个任务成果衡量标准上的表现。我们在开发能够进行社会认知并从事能够提高团队效率的协作行为的智能技术的背景下讨论了这些发现。
