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Abstract Automation is pervasive, and humans often collaborate with it to perform various tasks. With technological advancements, it has become increasingly important to understand the characteristics of complex teams composed of multiple humans and multiple agents. In this study, we examined various team-level metrics in multioperator, multiagent teams, including trust, communication, and task completion time (TCT). We conducted a human subjects experiment with 30 teams, each consisting of 2 human and 2 agent players, performing collaborative block-moving tasks. Each team completed 10 trials under 3 agent reliability pairing conditions: perfect (both agents had 100% reliability), mixed (1 agent had 100%, the other 50%), and imperfect (both agents had 50% reliability). Humans searched for target-colored blocks and issued commands to the agents, which followed predetermined logic to move the blocks to the drop zone. Participants’ trust ratings for each teammate and for the team were collected after each trial. From the experimental data, we quantified several team metrics: team-level trust in each referent (each teammate and the team), divergence of trust in each referent, team communication frequency, commands per block delivery, divergence of team communication, and TCT. We evaluated the effect of agent reliability pairing on each of these metrics and explored the associations between them. The results indicate strong interdependence: Shorter TCT is associated with higher team-level trust in team, greater agreement in trust ratings, and more effective, convergent communication patterns shared by the humans. This study offers important guidelines for evaluating human–agent teams at a higher, team-level perspective. 

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. 

Dynamic Stackelberg games are a broad class of two-player games in which the leader acts first, and the follower chooses a response strategy to the leader’s strategy. Unfortunately, only stylized Stackelberg games are explicitly solvable since the follower’s best-response operator (as a function of the control of the leader) is typically analytically intractable. This paper addresses this issue by showing that the follower’s best-response operator can be approximately implemented by an attention-based neural operator, uniformly on compact subsets of adapted open-loop controls for the leader. We further show that the value of the Stackelberg game where the follower uses the approximate best-response operator approximates the value of the original Stackelberg game. Our main result is obtained using our universal approximation theorem for attention-based neural operators between spaces of square-integrable adapted stochastic processes, as well as stability results for a general class of Stackelberg games. 

Abstract Salt marshes sequester a disproportionately large amount of carbon dioxide (CO₂) from the atmosphere through high rates of photosynthesis and carbon burial. Climate change could potentially alter this carbon sink, particularly the response of vegetation to environmental stressors that can decrease photosynthesis. Midday depression of gross primary production (GPP), characterized by a decline in photosynthesis during midday, has been documented in multiple ecosystems as a response to drought, high temperatures, and other stressors linked to climate change. Yet, midday depression has not been thoroughly investigated in salt marsh ecosystems. Here, we show that the midday depression of GPP in aSpartina alterniflorasalt marsh on the Eastern Shore of Virginia was ubiquitous and occurred on 76% of the 283 days studied during the 2019–2022 growing seasons. GPP was estimated from eddy covariance measurements with flux partitioning. Using random forest, we found that the daily maximum tidal height and air temperature were the strongest predictors of midday depression of GPP, with lower high tides and warmer temperatures associated with more severe depression. This result suggests midday depression occurs when GPP decreases in the afternoon in response to salinity and water stress. To our knowledge, this is the first examination of midday depression of photosynthesis inS.alternifloraat the ecosystem scale. Our results highlight the potential of climate change to increase midday depression of photosynthesis and ultimately weaken the salt marsh carbon sink. 

An ideal traffic simulator replicates the realistic long-term point-to-point trip that a self-driving system experiences during deployment. Prior models and benchmarks focus on closed-loop motion simulation for initial agents in a scene. This is problematic for long-term simulation. Agents enter and exit the scene as the ego vehicle enters new regions. We propose InfGen, a unified next-token prediction model that performs interleaved closed-loop motion simulation and scene generation. InfGen automatically switches between closed-loop motion simulation and scene generation mode. It enables stable long-term rollout simulation. InfGen performs at the state-of-the-art in short-term (9s) traffic simulation, and significantly outperforms all other methods in long-term (30s) simulation.