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1. Using Human-Agent Teams to Purposefully Design Multi-Agent Systems

   https://doi.org/10.1177/1071181319631238  

   Flathmann, Christopher; McNeese, Nathan; Canonico, Lorenzo Barberis (November 2019, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   With multi-agent teams becoming more of a reality every day, it is important to create a common design model for multi-agent teams. These teams need to be able to function in dynamic environments and still communicate with any humans that may need a problem solved. Existing human-agent research can be used to purposefully create multi-agent teams that are interdependent but can still interact with humans. Rather than creating dynamic agents, the most effective way to overcome the dynamic nature of modern workloads is to create a dynamic team configuration, rather than individual member-agents that can change their roles. Multi-agent teams will require a variety of agents to be designed to cover a diverse subset of problems that need to be solved in the modern workforce. A model based on existing multi-agent teams that satisfies the needs of human-agent teams has been created to serve as a baseline for human-interactive multi-agent teams. 

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2. The Impact of Training on Human–Autonomy Team Communications and Trust Calibration

   https://doi.org/10.1177/00187208211047323  

   Johnson, Craig J.; Demir, Mustafa; McNeese, Nathan J.; Gorman, Jamie C.; Wolff, Alexandra T.; Cooke, Nancy J. (October 2021, Human Factors: The Journal of the Human Factors and Ergonomics Society)

   Objective This work examines two human–autonomy team (HAT) training approaches that target communication and trust calibration to improve team effectiveness under degraded conditions. Background Human–autonomy teaming presents challenges to teamwork, some of which may be addressed through training. Factors vital to HAT performance include communication and calibrated trust. Method Thirty teams of three, including one confederate acting as an autonomous agent, received either entrainment-based coordination training, trust calibration training, or control training before executing a series of missions operating a simulated remotely piloted aircraft. Automation and autonomy failures simulating degraded conditions were injected during missions, and measures of team communication, trust, and task efficiency were collected. Results Teams receiving coordination training had higher communication anticipation ratios, took photos of targets faster, and overcame more autonomy failures. Although autonomy failures were introduced in all conditions, teams receiving the calibration training reported that their overall trust in the agent was more robust over time. However, they did not perform better than the control condition. Conclusions Training based on entrainment of communications, wherein introduction of timely information exchange through one team member has lasting effects throughout the team, was positively associated with improvements in HAT communications and performance under degraded conditions. Training that emphasized the shortcomings of the autonomous agent appeared to calibrate expectations and maintain trust. Applications Team training that includes an autonomous agent that models effective information exchange may positively impact team communication and coordination. Training that emphasizes the limitations of an autonomous agent may help calibrate trust. 

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3. Communication-as-Control: Intent-Aware Interaction for Scalable Multi-Agent Coordination

   https://doi.org/10.1609/aaaiss.v8i1.42528  

   Imani, Mahdi; Lan, Tian (May 2026, Proceedings of the AAAI Symposium Series)

   Uncrewed Aerial Systems (UAS) increasingly operate aslarge, heterogeneous teams under partial observability. Insuch settings, coordination depends on how agents infer,represent, and act upon uncertainty about one another:belief divergence is a natural consequence of asymmetricsensing and latent intent, and naive communicationmechanisms do not scale with team size. We argue for areframing of interaction as communication-as-control,grounded in a small set of tightly coupled capabilitiesthat link sensing to distributed execution underinteraction constraints. Rather than prescribing a fixedpipeline, coordination in such teams relies on severalinterdependent capabilities. First, agents must performimplicit intent inference without communication, forminguncertainty-aware beliefs over teammate intent and progressfrom observable behavior. Second, such uncertainty caninform selective explicit interaction, with targeted,minimal exchanges invoked only when unresolved ambiguitybecomes consequential for joint decision-making. Third,uncertainty arising from both implicit inference andexplicit interaction can be leveraged to supportbelief-guided coordination and adaptation when alignment ispartial, delayed, or unattainable. This makes selectiveinteraction a scalability mechanism: as teams grow in sizeand heterogeneity, coordination remains feasible only ifcommunication is sparse, recipient-specific, and tied todecision relevance. 

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4. Adaptive Agent Architecture for Real-time Human-Agent Teaming

   Ni, T. (January 2021, AAAI Workshop on Plan, Activity, and Intent Recognition)

   Teamwork is a set of interrelated reasoning, actions and behaviors of team members that facilitate common objectives. Teamwork theory and experiments have resulted in a set of states and processes for team effectiveness in both human-human and agent-agent teams. However, human-agent teaming is less well studied because it is so new and involves asymmetry in policy and intent not present in human teams. To optimize team performance in human-agent teaming, it is critical that agents infer human intent and adapt their polices for smooth coordination. Most literature in human-agent teaming builds agents referencing a learned human model. Though these agents are guaranteed to perform well with the learned model, they lay heavy assumptions on human policy such as optimality and consistency, which is unlikely in many real-world scenarios. In this paper, we propose a novel adaptive agent architecture in human-model-free setting on a two-player cooperative game, namely Team Space Fortress (TSF). Previous human-human team research have shown complementary policies in TSF game and diversity in human players’ skill, which encourages us to relax the assumptions on human policy. Therefore, we discard learning human models from human data, and instead use an adaptation strategy on a pre-trained library of exemplar policies composed of RL algorithms or rule-based methods with minimal assumptions of human behavior. The adaptation strategy relies on a novel similarity metric to infer human policy and then selects the most complementary policy in our library to maximize the team performance. The adaptive agent architecture can be deployed in real-time and generalize to any off-the-shelf static agents. We conducted human-agent experiments to evaluate the proposed adaptive agent framework, and demonstrated the suboptimality, diversity, and adaptability of human policies in human-agent teams. 

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5. A Systematic Review and Taxonomy of Human–Agent Teaming Testbeds

   https://doi.org/10.1177/00187208251376898  

   Chung, Hyesun; Holder, Timothy; Shah, Julie A; Yang, X Jessie (February 2026, Human Factors: The Journal of the Human Factors and Ergonomics Society)

   ObjectiveWe developed a taxonomy for human–agent teams (HATs) and conducted a literature review of existing HAT testbeds using our proposed taxonomy. BackgroundWith the increasing interest in HATs, numerous research studies in this field have utilized different testbeds. Despite this, there is a lack of comprehensive understanding regarding the capabilities and limitations of the existing testbeds. MethodWe first developed a taxonomy for HATs by modifying the existing framework for classifying human teams. Our proposed taxonomy comprises ten attributes. Subsequently, using the taxonomy, we analyzed 103 testbeds identified from 235 empirical research studies. After coding each testbed, we conducted frequency analyses on each attribute to determine the distribution of the testbeds. ResultsRegarding team composition, the majority of testbeds afford single human participants paired with few agents, typically in subordinate roles. Also, in most testbeds, the leadership structure is designated, with humans assuming leadership roles, or none. The communication dynamics present an area for further exploration, especially with larger team sizes. Additionally, nearly all reviewed testbeds focus on long-term teams, overlooking dynamics in ad hoc teams, which are common in real-world settings. ConclusionOur findings underscore the importance of further research into diverse team attributes, such as team composition, leadership structure, communication structure, direction, and medium. It would facilitate a deeper understanding of complex team dynamics in HATs and lead to designing effective teams. ApplicationThe current study would be valuable for discussing future research directions when developing new testbeds or designing novel experiments leveraging existing ones. 

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