EAGER:AI-DCL:Cognitive-Behavior Model to Predict Human Reaction to Swarm AI Non-Compliance.

EAGER：AI-DCL：预测人类对 Swarm AI 不合规反应的认知行为模型。

• 批准号: 1927462
• 负责人: Ehsan Esfahani
• 金额: $ 29.87万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1927462&HistoricalAwards=false
• 关键词: EAGER; AI; DCL; Cognitive; Behavior

This project studies autonomous swarm systems, which consist of a large number of collaborating robots that work together with a few human supervisors. Such systems promise unmatched task parallelism, fault-tolerance, resilience, and keeping humans out of harm's way. However, there are important technical issues that stand in the way of realizing these promises. Specifically, there is a sizeable lack of understanding of how human supervisors (who provide tactical input and mission intent, as opposed to direct or indirect control) interact with embodied swarm intelligence. That understanding is imperative for identifying solutions to mitigating cognitive overload and preserving human-swarm trust and shared situation awareness, especially in humans-swarm teams that are actually scalable to practical applications. The researchers will leverage their engagement with particular stakeholder groups including the Buffalo Fire Department and Government/Industrial parties involved in Oil Spill Response. The project will also have impact on education, training, and broader research community through the development of curricular materials for AI and Robotics courses, multi-robotic workshops at pertinent conferences, and public dissemination of data and models.The researchers will conduct a set of experiments and identify modeling approaches to answer fundamental questions regarding how humans respond to the behavior of embodied swarm agents. Those questions include how humans react to circumstantial or deliberate non-compliance by the swarm and the level of feedback provided by the swarm to explain any non-compliance, how humans identify and attribute errors (or perceived errors) to swarms, and how this impacts their intervention frequency. They will use a neuro-ergonomic approach to estimate human metrics such as event-related (brain) potential, intervention tendencies, situation awareness, and cognitive workload via physiological information. On the machine intelligence side, they will design decentralized swarm behavior to allow studying impact of unique factors such as non-compliance and feedback levels in search and object transport applications. The researchers have complementary expertise in the areas of human-robot interaction, human psycho-physiological monitoring, and autonomous swarm systems for engaging in the synergistic research activities that are central to the project. They also have access to key research facilities including a large motion capture environment, a swarmbot arena, aerial/ground swarm robotic platforms, brain-computer interfaces, and physiological monitoring setups.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目研究自治群系统，它由大量协作机器人组成，与少数人类监督者一起工作。这样的系统保证了无与伦比的任务并行性、容错性、弹性，并使人类免受伤害。然而，存在一些重要的技术问题阻碍了这些承诺的实现。具体来说，人们对人类管理者（提供战术输入和使命意图，而不是直接或间接控制）如何与具体的群体智能互动缺乏相当大的了解。这种理解对于确定缓解认知过载、保持人类群体信任和共享态势感知的解决方案至关重要，特别是在实际可扩展到实际应用的人类群体团队中。研究人员将利用他们与特定利益相关者群体的接触，包括布法罗消防局和参与漏油反应的政府/工业方。该项目还将通过开发人工智能和机器人课程的课程材料、在相关会议上举办多机器人研讨会以及公开传播数据和模型，对教育、培训和更广泛的研究社区产生影响。研究人员将进行一系列实验，并确定建模方法，以回答有关人类如何对具体群体代理行为做出反应的基本问题。这些问题包括人类如何对群体的间接或故意不遵守行为做出反应，以及群体为解释任何不遵守行为而提供的反馈水平，人类如何识别和归因于错误（或感知错误），以及这如何影响他们的干预频率。他们将使用神经人体工程学方法来估计人类指标，如事件相关（大脑）潜力，干预倾向，情境意识和通过生理信息的认知工作量。在机器智能方面，他们将设计分散的群体行为，以研究独特因素的影响，例如搜索和对象传输应用中的不合规和反馈水平。研究人员在人机交互，人类心理生理监测和自主群系统领域具有互补的专业知识，用于参与该项目核心的协同研究活动。他们还可以使用关键的研究设施，包括大型动作捕捉环境、群机器人竞技场、空中/地面群机器人平台、脑机接口和生理监测设置。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

An Extended Bayesian Optimization Approach to Decentralized Swarm Robotic Search

• DOI: 10.1115/1.4046587
• 发表时间: 2020-04
• 期刊: J. Comput. Inf. Sci. Eng.
• 作者: P. Ghassemi;Souma Chowdhury

Using Physiological Information to Classify Task Difficulty in Human-Swarm Interaction

利用生理信息对人群交互中的任务难度进行分类

• DOI: 10.1109/smc52423.2021.9658653
• 发表时间: 2021
• 期刊: and Cybernetics (SMC
• 作者: Distefano, Joseph P.;Manjunatha, Hemanth;Chowdhury, Souma;Dantu, Karthik;Doermann, David;Esfahani, Ehsan T.
• 通讯作者: Esfahani, Ehsan T.

Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas

• DOI: 10.1109/icra48506.2021.9561550
• 发表时间: 2021-03
• 期刊: 2021 IEEE International Conference on Robotics and Automation (ICRA)
• 作者: Leighton Collins;P. Ghassemi;E. Esfahani;D. Doermann;Karthik Dantu;Souma Chowdhury

Learning Robot Swarm Tactics over Complex Adversarial Environments

• DOI: 10.1109/mrs50823.2021.9620707
• 发表时间: 2021-09
• 期刊: 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)
• 作者: A. Behjat;H. Manjunatha;Prajit KrisshnaKumar;Apurv Jani;Leighton Collins;P. Ghassemi;Joseph P. Distefano-Joseph

Framework for Analyzing Human Cognition in Operationally-Relevant Human Swarm Interaction

与操作相关的人群交互中的人类认知分析框架

• 发表时间: 2023
• 期刊: 2023 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
• 作者: Distefano, Joseph;Cho, Henry;KrisshnaKumar, Prajit;Chowdhury, Souma;Esfahani, Ehsan
• 通讯作者: Esfahani, Ehsan