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1. 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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2. Sleep Patterns and Sleep Alignment in Remote Teams during COVID-19

   https://doi.org/10.1145/3555217  

   Breideband, Thomas ; Martinez, Gonzalo J. ; Talkad Sukumar, Poorna ; Caruso, Megan ; D'Mello, Sidney ; Striegel, Aaron D. ; Mark, Gloria ( November 2022 , Proceedings of the ACM on Human-Computer Interaction: Computer Supported Collaborative Work)

   Working remotely from home during the COVID-19 pandemic has resulted in significant shifts and disruptions in the personal and work lives of millions of information workers and their teams. We examined how sleep patterns---an important component of mental and physical health---relates to teamwork. We used wearable sensing and daily questionnaires to examine sleep patterns, affect, and perceptions of teamwork in 71 information workers from 22 teams over a ten-week period. Participants reported delays in sleep onset and offset as well as longer sleep duration during the pandemic. A similar shift was found in work schedules, though total work hours did not change significantly. Surprisingly, we found that more sleep was negatively related to positive affect, perceptions of teamwork, and perceptions of team productivity. However, a greater misalignment in the sleep patterns of members in a team predicted positive affect and teamwork after accounting for individual differences in sleep preferences. A follow-up analysis of exit interviews with participants revealed team-working conventions and collaborative mindsets as prominent themes that might help explain some of the ways that misalignment in sleep can affect teamwork. We discuss implications of sleep and sleep misalignment in work-from-home contexts with an eye towards leveraging sleep data to facilitate remote teamwork.

    

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3. Trembling-Hand Perfection in Extensive-Form Games with Commitment

   https://doi.org/10.24963/ijcai.2018/32  

   Farina, Gabriele ; Marchesi, Alberto ; Kroer, Christian ; Gatti, Nicola ; Sandholm, Tuomas ( July 2018 , International Joint Conferences on Artificial Intelligence Organization)

   We initiate the study of equilibrium refinements based on trembling-hand perfection in extensive-form games with commitment strategies, that is, where one player commits to a strategy first. We show that the standard strong (and weak) Stackelberg equilibria are not suitable for trembling-hand perfection, because the limit of a sequence of such strong (weak) Stackelberg commitment strategies of a perturbed game may not be a strong (weak) Stackelberg equilibrium itself. However, we show that the universal set of all Stackelberg equilibria (i.e., those that are optimal for at least some follower response function) is natural for trembling- hand perfection: it does not suffer from the problem above. We also prove that determining the existence of a Stackelberg equilibrium--refined or not--that gives the leader expected value at least v is NP-hard. This significantly extends prior complexity results that were specific to strong Stackelberg equilibrium. 

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4. Operationalizing team commitment in a project-based learning environment

   https://doi.org/10.1109/FIE56618.2022.9962577  

   Jaiswal, Aparajita ; Karabiyik, Tugba ; Thomas, Paul ; Magana, Alejandra J. ( October 2022 , Proceedings of the Frontiers in Education Conference (FIE))

   Commitment is a multi-dimensional construct that has been extensively researched in the context of organizations. Organizational and professional commitment have been positively associated with technical performance, client service, attention to detail, and degree of involvement with one’s job. However, there is a relative dearth of research in terms of team commitment, especially in educational settings. Teamwork is considered a 21stcentury skill and higher education institutions are focusing on helping students to develop teamwork skills by applied projects in the coursework. But studies have demonstrated that creating a team is not enough to help students build teamwork skills. Literature supports the use of team contracts to bolster commitment, among team members. However, the relationship between team contracts and team commitment has not been formally operationalized.This research category study presents a mixed-methods approach towards characterizing and operationalizing team commitment exhibited by students enrolled in a sophomore-level systems analysis and design course by analyzing team contracts and team retrospective reflections. The course covers concepts pertaining to information systems development and includes a semester-long team project where the students work together in four or five member teams to develop the project deliverables. The students have prior software development experiences through an introductory systems development course as well as multiple programming courses. The data for this study was collected through the team contracts signed by students belonging to one of the 23 teams of this course. The study aims to answer the following research question: How can team commitment be characterized in a sophomore-level system analysis and design course among the student teams?A rubric was developed to quantify the team commitment levels of students based on their responses on the team contracts. Students were classified as high or low commitment based on the rubric scores. The emergent themes of high and low commitment teams were also presented. The results indicated that the high commitment teams were focused on setting goals, effective communication, and having mechanisms in place for timely feedback and improvement. On the other hand, low commitment teams did not articulate the goals of the project, they demonstrated a lack of dedication for attending team meetings regularly, working as a team, and had a lack of proper coordination while working together. 

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5. Intelligent Distributed Swarm Control for Large-Scale Multi-UAV Systems: A Hierarchical Learning Approach

   https://doi.org/10.3390/electronics12010089  

   Dey, Shawon ; Xu, Hao ( January 2023 , Electronics)

   In this paper, a distributed swarm control problem is studied for large-scale multi-agent systems (LS-MASs). Different than classical multi-agent systems, an LS-MAS brings new challenges to control design due to its large number of agents. It might be more difficult for developing the appropriate control to achieve complicated missions such as collective swarming. To address these challenges, a novel mixed game theory is developed with a hierarchical learning algorithm. In the mixed game, the LS-MAS is represented as a multi-group, large-scale leader–follower system. Then, a cooperative game is used to formulate the distributed swarm control for multi-group leaders, and a Stackelberg game is utilized to couple the leaders and their large-scale followers effectively. Using the interaction between leaders and followers, the mean field game is used to continue the collective swarm behavior from leaders to followers smoothly without raising the computational complexity or communication traffic. Moreover, a hierarchical learning algorithm is designed to learn the intelligent optimal distributed swarm control for multi-group leader–follower systems. Specifically, a multi-agent actor–critic algorithm is developed for obtaining the distributed optimal swarm control for multi-group leaders first. Furthermore, an actor–critic–mass method is designed to find the decentralized swarm control for large-scale followers. Eventually, a series of numerical simulations and a Lyapunov stability proof of the closed-loop system are conducted to demonstrate the performance of the developed scheme. 

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