EAGER: Hybrid Socio-Technical Teams: A Theoretical Framework for Modeling and Design of Hybrid Networks of Human and Autonomous Agents

EAGER：混合社会技术团队：人类和自主代理混合网络建模和设计的理论框架

• 批准号: 1548521
• 负责人: Babak Heydari
• 金额: $ 22.99万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1548521&HistoricalAwards=false
• 关键词: EAGER; Hybrid; Socio; Technical; Teams

Many critical systems in the future will rely on coordinated teamwork of a hybrid group of humans and smart, decision-making, autonomous agents. Such hybrid teams are becoming increasingly common in a wide variety of socio-technical systems. Members of such teams are highly heterogeneous, work in rapidly changing and uncertain environments, have different goals and motivations, and are often spatially distributed with little direct face-to-face interaction. This creates serious challenges for team-level coordination and cooperation, and can result in decreased efficiency, or even disastrous failure, in some critical cases. This award supports fundamental research to provide needed understanding of dynamics, behavior, and coordination mechanisms of human-agent networks. Outcomes of this project will result in better understanding of the behavior of such networks, and more efficient design of future intelligent systems for disaster response, energy and transportation, all of which will in the future rely on efficient, dynamic coordination between a group of people and a network of autonomous agents. Understanding hybrid teams and addressing their coordination challenges require an interdisciplinary approach that combines computational and behavioral sciences. To this end, this research will combine multi-agent computational methods such as complex network analysis and game theory with social psychology of team behavior. The research team will create parametric, context-dependent, analytical and computational models to understand how team-level coordination and cooperative behavior are affected by variables such as composition of team members, communication network structure among nodes, distribution of humans and machines on networks nodes, and key parameters in the environment. Analytical methods, such as game theory on graphs, and verifiable agent-based simulations will be used to draw causal inferences from the network structure and agent compositions to overall team-level performance parameters. The research will also test the hypothesis that the connectivity structure of the team network can be used as an effective control parameter to dynamically steer the cooperative behavior of hybrid socio-technical systems. To validate the developed models, the research team will perform a data-driven study of disaster response teams. It will investigate the impact on overall coordination and efficiency when introducing autonomous agents into human disaster-response teams.

未来的许多关键系统将依赖于人类和智能、决策、自主代理的混合团队的协调合作。这种混合团队在各种社会技术系统中越来越普遍。这些团队的成员是高度异质性的，在快速变化和不确定的环境中工作，有不同的目标和动机，并且通常在空间上分布，很少有直接的面对面的互动。这给团队一级的协调与合作带来了严重的挑战，并可能导致效率下降，甚至在某些关键情况下造成灾难性的失败。该奖项支持基础研究，以提供对人类智能体网络的动力学，行为和协调机制的必要理解。该项目的成果将使人们更好地理解这种网络的行为，并更有效地设计未来的灾害响应、能源和交通智能系统，所有这些都将依赖于一群人和一个自治代理网络之间的高效、动态协调。了解混合团队并解决其协调挑战需要结合计算和行为科学的跨学科方法。为此，本研究将联合收割机多智能体计算方法如复杂网络分析和博弈论与团队行为的社会心理学相结合。研究团队将创建参数化、上下文相关、分析和计算模型，以了解团队协调和合作行为如何受到团队成员组成、节点间通信网络结构、网络节点上人类和机器分布以及环境中关键参数等变量的影响。分析方法，如博弈论的图形，和可验证的代理人为基础的模拟将被用来绘制因果关系的推论，从网络结构和代理组成的整体团队水平的性能参数。研究还将测试的假设，团队网络的连接结构可以作为一个有效的控制参数，以动态地引导混合社会技术系统的合作行为。为了验证所开发的模型，研究小组将对救灾小组进行数据驱动研究。 它将调查在人类灾害响应团队中引入自主代理时对整体协调和效率的影响。