A new research paper explores how time-varying interactions, modeled as a Markov switching graph, affect the robustness of multi-agent systems dealing with noise. The study, using Markov jump linear systems, derives expressions for steady-state deviations and tracking errors to measure performance based on interaction graphs and switching dynamics. It extends existing robustness concepts to the Markov switching graph setting and analyzes how switching topologies influence system performance in coordination tasks. AI
IMPACT Provides a theoretical framework for understanding agent coordination under dynamic conditions, potentially informing future multi-agent AI system design.
RANK_REASON The cluster contains a single academic paper submission to arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.MA (Multiagent) →
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