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<p>Multi-agent robotic systems (MARS) are well-suited for collective tasks; however, their efficient operation in decentralized settings requires inter-agent communication for coordination. While global broadcasting enables complete information sharing, it incurs substantial transmission power and bandwidth costs. Consequently, recent studies have proposed algorithms that operate under partial communication or limited communication ranges. These approaches typically rely on local planning or learning-based agents to compensate for missing or incomplete information, and evaluate performance degradation primarily as a function of communication range. In MARS, both the frequency of communication and the content of exchanged information significantly affect collective performance; however, these factors are often treated implicitly and rarely studied as explicit parameters. In this work, we introduce a <i>dual-variable impact analysis</i> that explicitly considers both <i>communication range</i> and <i>communication content</i>. Rather than compensating for information loss through complex planning or learning mechanisms, we isolate and examine how communication constraints directly influence collective performance. Using simple rule-based agents that perform cooperative area coverage with explicit knowledge-sharing and reasoning, we systematically evaluate how much performance can be recovered through minimal communication mechanisms. Our results demonstrate that appropriately designed communication content can partially recover information loss and maintain task efficiency even when agents are outside the direct communication range.</p>

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Communication constraints and information exchange in decentralized multi-agent systems

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Dhamdhawach Horsuwan,
  • Yves Demazeau,
  • Poramate Manoonpong

摘要

Multi-agent robotic systems (MARS) are well-suited for collective tasks; however, their efficient operation in decentralized settings requires inter-agent communication for coordination. While global broadcasting enables complete information sharing, it incurs substantial transmission power and bandwidth costs. Consequently, recent studies have proposed algorithms that operate under partial communication or limited communication ranges. These approaches typically rely on local planning or learning-based agents to compensate for missing or incomplete information, and evaluate performance degradation primarily as a function of communication range. In MARS, both the frequency of communication and the content of exchanged information significantly affect collective performance; however, these factors are often treated implicitly and rarely studied as explicit parameters. In this work, we introduce a dual-variable impact analysis that explicitly considers both communication range and communication content. Rather than compensating for information loss through complex planning or learning mechanisms, we isolate and examine how communication constraints directly influence collective performance. Using simple rule-based agents that perform cooperative area coverage with explicit knowledge-sharing and reasoning, we systematically evaluate how much performance can be recovered through minimal communication mechanisms. Our results demonstrate that appropriately designed communication content can partially recover information loss and maintain task efficiency even when agents are outside the direct communication range.