We enable efficient and effective coordination in unpredictable environments, ie, in
environments whose future evolution is unknown a priori and even adversarial. We are …

Multi-robot decision-making is the process where multiple robots coordinate actions. In this
paper, we aim for efficient and effective multi-robot decision-making despite the robots' …

We study the problem of multi-agent coordination in unpredictable and partially observable
environments, that is, environments whose future evolution is unknown a priori and that can …

We introduce the first, to our knowledge, rigorous approach that enables multi-agent
networks to self-configure their communication topology to balance the trade-off between …

Joint utility-maximization problems for multi-agent systems often should be addressed by
distributed strategy-selection formulation. Constrained by discrete feasible strategy sets …

We study the problem of multi-agent coordination in unpredictable and partially-observable
environments with untrustworthy external commands. The commands are actions suggested …

We provide a distributed coordination paradigm that enables scalable and near-optimal joint
motion planning among multiple robots. Our coordination paradigm contrasts with current …

This paper proposes a greedy stochastic optimization algorithm for the sensor set
decomposition used in the sensor fault monitoring of marine propulsion systems, based on …

This article provides a comprehensive exploration of submodular maximization problems,
focusing on those subject to uniform and partition matroids. Crucial for a wide array of …

This paper considers a multi-agent submodular set function maximization problem subject to
partition matroid in which the utility is shared, but the agents' policy choices are constrained …