This article presents a learning-based algorithm for solving the online source-seeking
problem with a multiagent system under an unknown dynamical environment. Our algorithm …

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' …

This paper presents an algorithmic framework for the distributed on-line source seeking,
termed as DoSS, with a multi-robot system in an unknown dynamical environment. Our …

In this work, we study the multi-agent decision problem where agents try to coordinate to
optimize a given system-level objective. While solving for the global optimum is intractable in …

We consider a class of discrete optimization problems that aim to maximize a submodular
objective function subject to a distributed partition matroid constraint. More precisely, we …

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 …

In multi-robot systems, robots often gather data to improve the performance of their deep
neural networks (DNNs) for perception and planning. Ideally, these robots should select the …

In this piece, we investigate a full duplex (FD) network where nonorthogonal multi-access
(NOMA) is used in both uplink and downlink broadcasts in order to exploit spectrum usage …

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 investigates dynamic task allocation for multi-agent systems (MASs) under
resource constraints, with a focus on maximizing the global utility of agents while ensuring a …