There is an increasing need for automated support for humans monitoring the activity of distributed teams of cooperating agents, both human and machine. We characterize the domain-independent challenges posed by this problem, and describe how properties of domains influence the challenges and their solutions. We will concentrate on dynamic, data-rich domains where humans are ultimately responsible for team behavior. Thus, the automated aid should interactively support effective and timely decision making by the human. We present a domain-independent categorization of the types of alerts a plan-based monitoring system might issue to a user, where each type generally requires different monitoring techniques. We describe a monitoring framework for integrating many domain-specific and task-specific monitoring techniques and then using the concept of value of an alert to avoid operator overload. We use this framework to describe an execution monitoring approach we have used to implement Execution Assistants (EAs) in two different dynamic, data-rich, real-world domains to assist a human in monitoring team behavior. One domain (Army small unit operations) has hundreds of mobile, geographically distributed agents, a combination of humans, robots, and vehicles. The other domain (teams of unmanned ground and air vehicles) has a handful of cooperating robots. Both domains involve unpredictable adversaries in the vicinity. Our approach customizes monitoring behavior for each specific task, plan, and situation, as well as for user preferences. Our EAs alert the human controller when reported events threaten plan execution or physically threaten team members. Alerts were generated in a timely manner without inundating the user with too many alerts (less than 10 percent of alerts are unwanted, as judged by domain experts).
Think How Your Teammates Think: Active Inference Can Benefit Decentralized Execution
School of Computer Science & Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence（北京市交通数据挖掘与具身智能重点实验室）
Deep Generative Multi-Agent Imitation Model as a Computational Benchmark for Evaluating Human Performance in Complex Interactive Tasks: A Case Study in Football
A Hybrid Agent-Based and System Dynamics Framework for Modelling Project Execution and Technology Maturity in Early-Stage R&D
Norwegian University of Science and Technology（挪威科技大学）；CENPES, Petrobras R&D Center（巴西国家石油公司研发中心）；Federal University of Bahia, Polytechnic School（巴伊亚联邦大学理工学院）
PC3D: Zero-Shot Cooperation Across Variable Rosters via Personalized Context Distillation
Doctoral School of Exact and Natural Sciences, Jagiellonian University（杰哥利昂大学精确与自然科学博士学院）；Faculty of Mathematics and Computer Science, Jagiellonian University（杰哥利昂大学数学与计算机科学学院）
