Integration of Humans and Systems

Includes basic and applied research that aims to discover, understand, exploit, and apply fundamental principles of integrating humans and systems across domains, including but not limited to complex information systems, human-agent teams, cybersecurity, and organizational and social networks.  Discoveries of fundamental principles governing networked communications and human-system relationships and dynamics are expected to lead to technological and methodological innovations critical in poising the Army of 2030 to quickly shape its operational environment. These discoveries are expected to be relevant across the full range of social and cultural environments.

Integration Technologies

Manned and Unmanned Collaborative Systems Integration

Advancements in technology have greatly improved the capability of military systems. Mission requirements now dictate system interoperability levels that go beyond traditional human-machine interface paradigms. Asymmetric threats are dictating collaborations among multiple systems. These higher-order system-of-system interactions are introducing challenges to the way technology is designed, constructed, measured, and evaluated. ARL’s research seeks to expand our understanding of the dynamic human-machine relationships already deployed and underway with the military’s fleet of optionally piloted vehicles. Additional collaborations are sought to further this research.  Core research initiatives address the demands placed on the Soldiers’ mental resources required to manage attention, make decisions, and coordinate crew activities and communication.

Principal Investigator:

Dr. Tom Davis,, (256) 876-2048

Human-Agent Teaming

The goal of the Human-Robot Interaction (HRI) program is to maximize the effectiveness of integrating multiple, networked autonomous, intelligent systems technology into the Soldier team and operations through the development of state-of-the-art (or beyond) Soldier-system interactions. We seek to identify tools, techniques, and measures that can be used to improve and assess performance for human-system teams.  We are interested in research results that may be applicable across environments, operations, and platforms.  Our HRI program focuses on Soldier-system teaming, Soldier-system bi-directional communications, and Soldier-system performance.  We are specifically interested in Open Campus partners who have expertise in the following areas:

- Naturalistic bi-directional communication: development of multimodal technologies and user interface design; multi-team member or multi-node networked communication paradigms; and algorithm development that support both receipt and transmittal of appropriate and timely messages or data between both human and machine agents.

- HRI metrics development: Assessing subjective, behavioral, and physiological indicators of trust, workload, situation awareness, etc. and implications on teaming performance and teaming behaviors.

- Manned-unmanned teaming: mission command for robotic and agent assets; multi-node networked coordination of agents; user interface designs that support multi-agent teaming; principles and paradigm development of multi-agent coordination in both ideal and degraded environments.

- Impact of social and behavioral norms on HRI: assessing the relationships between normative behaviors and situation awareness; using norms to facilitate predictable behaviors in human-agent teams.

Principal Investigators:

Dr. Susan G. Hill,, (410) 278-5982
Dr. A. William Evans,, (410) 278-5982

Humans in Multi-Agent Systems

Human Cybersecurity

Advancing the human sciences to support the cyber analyst is a key link in the Army's cyber defense strategy. ARL's research in this area encompasses four main thrusts: (1) Cyber Cognition and Biopsychology: the development of metric approaches to quantify and predict cyber analyst performance and human-system interactions; leverages current networking technology and recent advances in "wearables" technology enabling researchers to collect, extract, and analyze large volumes of time-stamped data to characterize high resolution behavioral, physiological, task-based, and environmental factors influencing task performance and decision-making of individuals and teams, (2) Training Effectiveness: addresses the challenge currently facing the maturation of cyber capable defense forces: understanding how to challenge, assess, and rapidly develop cyber skill-sets in realistic cyber operational environments, (3) Cyber Team Processes: focuses on capturing and understanding team processes and dynamics to achieve greater mission effectiveness, and (4) Adversarial Dynamics: focuses on modeling and simulation to understand adversarial attacker-defender-user dynamics in the cyber domain using approaches such as game theory, artificial intelligence, cognitive modeling, and multi-agent simulation.  Collaborations are desired (1) in furthering knowledge of the interplay of attackers-defenders-users; (2) on methods to focus on either the attacker or the defender (Intel analyst); (3) on methods to create training scenarios and measure training effectiveness.  Modeling approaches, as well as opportunities in field exercises or cyber challenges, or laboratory studies are sought. 

Principal Investigator:

Dr. Norbou Buchler,, (410) 278-9403

Visualizing Sociocultural Data in 2D and 3D Immersive Environments to Improve Situational Understanding and Decision-Making for Civil Information

Understanding sociocultural factors and their influence on mission success is important for effective decision-making in military, peace-keeping, and stability roles of those that generate, analyze, and disseminate civil information. However, gaps exist in the collection, analysis, and dissemination of sociocultural information that hinder the maximal successful use of such data. For example, sociocultural data can be dynamic or static, location-based or not, categorical or continuous, and sparse or dense data. The complexity of sociocultural and civil information data, relationships among variables, and the influence of these variables on mission success contribute to the underutilization of sociocultural data. One method the Army uses to understand the operational environment is by visualizing data. Data visualization has been championed as a method to gain insight into data and create actionable knowledge relevant to the current mission. The Army is interested in facilitating the knowledge generation, insight, and understanding process for decision making process by developing visualization methods and guidelines for civil information data through visualizing complex sociocultural data and unique ways of interacting with that data. We are conducting empirical behavioral research to better understand and validate how various visualization methods and displays may improve situational understanding and aid decision-making for those Army entities that are responsible for civil information. Ultimately, understanding these visualization methods will enhance decision-making within the U.S. Army and multi-partner and coalition teams as well as improve forecasting and prediction of indigenous civil activity. The current research team includes and is interested in working with computer scientists, geographic information scientists, experts in predictive algorithms, cognitive psychologists, and sociologists or anthropologists.

Principal Investigators: 

Dr. Christopher Garneau,, (410) 278-5814
Dr. Michael Geuss,, (410) 278-5892

Joint decision-making in human-agent teams

Our research aims to fuse advancements in human computation and artificial intelligence to support joint decision-making in teams of humans and intelligent agents. Soldiers on the future battlefield will be networked with sensors, robots, knowledge bases, and other Soldiers. By leveraging the unique strengths of each member of this team, we want to achieve superior performance in the collective. In support of this future capability, we pursue basic research into decision-making and task allocation algorithms using approaches from reinforcement learning, distributed optimization, and multi-modal deep learning. Additionally, we conduct applied research toward proof-of-concept technologies for applications in a variety of domains. We invite collaborations in algorithm development, system development, and novel applications for such approaches. This effort supports the Human-Agent Teaming and the Artificial Intelligence and Machine Learning Essential Research Areas.

Principal Investigator:

Addison Bohannon,, (410) 278-8974