139,775 research outputs found

    Nonconvex Distributed Feedback Optimization for Aggregative Cooperative Robotics

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    Distributed aggregative optimization is a recently emerged framework in which the agents of a network want to minimize the sum of local objective functions, each one depending on the agent decision variable (e.g., the local position of a team of robots) and an aggregation of all the agents' variables (e.g., the team barycentre). In this paper, we address a distributed feedback optimization framework in which agents implement a local (distributed) policy to reach a steady-state minimizing an aggregative cost function. We propose Aggregative Tracking Feedback, i.e., a novel distributed feedback optimization law in which each agent combines a closed-loop gradient flow with a consensus-based dynamic compensator reconstructing the missing global information. By using tools from system theory, we prove that Aggregative Tracking Feedback steers the network to a stationary point of an aggregative optimization problem with (possibly) nonconvex objective function. The effectiveness of the proposed method is validated through numerical simulations on a multi-robot surveillance scenario

    Culture change in elite sport performance teams: Examining and advancing effectiveness in the new era

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    Reflecting the importance of optimizing culture for elite teams, Fletcher and Arnold (2011) recently suggested the need for expertise in culture change. Acknowledging the dearth of literature on the specific process, however, the potential effectiveness of practitioners in this area is unknown. The present paper examines the activity's precise demands and the validity of understanding in sport psychology and organizational research to support its delivery. Recognizing that sport psychologists are being increasingly utilized by elite team management, initial evidence-based guidelines are presented. Finally, to stimulate the development of ecologically valid, practically meaningful knowledge, the paper identifies a number of future research directions

    Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems

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    A key challenge in multi-robot and multi-agent systems is generating solutions that are robust to other self-interested or even adversarial parties who actively try to prevent the agents from achieving their goals. The practicality of existing works addressing this challenge is limited to only small-scale synchronous decision-making scenarios or a single agent planning its best response against a single adversary with fixed, procedurally characterized strategies. In contrast this paper considers a more realistic class of problems where a team of asynchronous agents with limited observation and communication capabilities need to compete against multiple strategic adversaries with changing strategies. This problem necessitates agents that can coordinate to detect changes in adversary strategies and plan the best response accordingly. Our approach first optimizes a set of stratagems that represent these best responses. These optimized stratagems are then integrated into a unified policy that can detect and respond when the adversaries change their strategies. The near-optimality of the proposed framework is established theoretically as well as demonstrated empirically in simulation and hardware
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