CayleyTopo uses reinforcement learning to optimize Cayley graph generators for lower diameter, yielding faster and more resilient information flow in multi-agent systems than hand-crafted sparse topologies.
Consensus and coop- eration in networked multi-agent systems
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
The paper derives feedback conditions that violate topology identifiability for partial and full observations and proposes a distributed design that trades consensus deviation against topology privacy under limited budgets.
LiFE-CD computes the full probability distribution of convergence time for max-consensus over networks with Bernoulli link failures, exactly for acyclic graphs and with tight bounds for cyclic ones via spanning tree reduction.
A decentralized control framework using time-varying nonsmooth Lyapunov functions lets each agent satisfy only a local constraint, while the sum still converges, including exact decentralized coverage of time-varying densities.
A survey comparing classical multi-agent systems with large foundation model-enabled multi-agent systems, showing how the latter enables semantic-level collaboration and greater adaptability.
citing papers explorer
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Cayley Graph Optimization for Scalable Multi-Agent Communication Topologies
CayleyTopo uses reinforcement learning to optimize Cayley graph generators for lower diameter, yielding faster and more resilient information flow in multi-agent systems than hand-crafted sparse topologies.
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Preserving Topology Privacy of Network Systems by Feedback: Conditions and Distributed Design
The paper derives feedback conditions that violate topology identifiability for partial and full observations and proposes a distributed design that trades consensus deviation against topology privacy under limited budgets.
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Convergence Time Distributions for Max-Consensus over Unreliable Networks
LiFE-CD computes the full probability distribution of convergence time for max-consensus over networks with Bernoulli link failures, exactly for acyclic graphs and with tight bounds for cyclic ones via spanning tree reduction.
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Disentangled Control of Multi-Agent Systems
A decentralized control framework using time-varying nonsmooth Lyapunov functions lets each agent satisfy only a local constraint, while the sum still converges, including exact decentralized coverage of time-varying densities.
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Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures
A survey comparing classical multi-agent systems with large foundation model-enabled multi-agent systems, showing how the latter enables semantic-level collaboration and greater adaptability.