Modeling teamwork of multi-human multi-agent teams

Teamwork is important when humans work together with automated agents to perform tasks requiring monitoring, coordination, and complex decision making. While human-agent teams can bring many benefits such as higher productivity, adaptability and creativity, they may also fail for various reasons. It is important to understand the tradeoffs in teamwork. The purpose of this research is to investigate the process and outcomes of human-agent teamwork by running experiments and building quantitative simulation models. Preliminary results are discussed as well as future directions. We expect this research to deepen the under-standing of human-agent teamwork and provide recommendations for the design of teams and agents to support teamwork.This research is sponsored by the Office for Naval Research and the Air Force Office of Scientific Research

Designing for Work with Intelligent Entities: A Review of Perspectives

As the power of Artificial Intelligence (AI) continues to advance, there is increased interest in how best to combine AI-based agents with humans to achieve mission effectiveness. Three perspectives have emerged. The first stems from more conventional human factors traditions and views these entities as highly capable tools that humans can use to accomplish increasingly sophisticated tasks. The second "camp" believes that as the sophistication of these entities increases, it becomes increasingly appropriate to talk about them as "teammates" and use the research on human teams as a foundation for further exploration. The third perspective is emerging and finds both the "tools" and "teammate" metaphors flawed and limiting. This perspective emphasizes "joint activity," "joint cognitive activity," or something similar. In this article, we briefly review these three perspectives

A Hierarchical Variable Autonomy Mixed-Initiative Framework for Human-Robot Teaming in Mobile Robotics

This paper presents a Mixed-Initiative (MI) framework for addressing the problem of control authority transfer between a remote human operator and an AI agent when cooperatively controlling a mobile robot. Our Hierarchical Expert-guided Mixed-Initiative Control Switcher (HierEMICS) leverages information on the human operator's state and intent. The control switching policies are based on a criticality hierarchy. An experimental evaluation was conducted in a high-fidelity simulated disaster response and remote inspection scenario, comparing HierEMICS with a state-of-the-art Expert-guided Mixed-Initiative Control Switcher (EMICS) in the context of mobile robot navigation. Results suggest that HierEMICS reduces conflicts for control between the human and the AI agent, which is a fundamental challenge in both the MI control paradigm and also in the related shared control paradigm. Additionally, we provide statistically significant evidence of improved, navigational safety (i.e., fewer collisions), LOA switching efficiency, and conflict for control reduction.Comment: 6 pages, 4 figures, ICHMS 2022, First two Authors contributed equall

The Internet of Robotic Things:A review of concept, added value and applications

The Internet of Robotic Things is an emerging vision that brings together pervasive sensors and objects with robotic and autonomous systems. This survey examines how the merger of robotic and Internet of Things technologies will advance the abilities of both the current Internet of Things and the current robotic systems, thus enabling the creation of new, potentially disruptive services. We discuss some of the new technological challenges created by this merger and conclude that a truly holistic view is needed but currently lacking.Funding Agency:imec ACTHINGS High Impact initiative</p