熵门控信念协调用于间歇通信下的去中心化多智能体搜索
Entropy-Gated Belief Coordination for Decentralized Multi-Agent Search Under Intermittent Communication
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- Worcester Polytechnic Institute（伍斯特理工学院）
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中文总结 AI 辅助
针对间歇通信下多智能体搜索，提出熵门控信念协调，通过阈值控制融合时机，EG-TDP在实验中显著提升信念质量并优于联合贝叶斯参考。
中文摘要 AI 辅助
我们研究去中心化多智能体目标搜索问题，其中同质智能体在泊松分布的时间点进行间歇性通信。标准的无条件信念融合会浪费通信机会，因为在高熵探索阶段同步智能体时，多样化的独立信念比过早的共识能提供更好的覆盖。我们提出熵门控信念协调，其中智能体在集体熵比率超过阈值θ时跳过融合，仅在利用阶段进行合并，这与非线性意见动态的分岔结构以及每步信息增益目标的子模结构一致。我们进一步推导出Istep(α,β)，即两个智能体二元传感器之间每观测步的期望互信息，作为一种可解释的、无需通信的传感器信息量度量，它为门控设计提供动机并指导系统级分析。在103,680次试验（九种网格尺寸，最大至100×100，泊松通信时序，四种目标移动模式）中的实验表明，熵门控信任衰减规划器（EG-TDP）在利用模式中增加了检测概率规划器开关，实现了平均信念质量Q̄=0.300（真实目标单元格的平均信念质量，在所有试验和步骤中平均），比算术平均融合提高了58.9%，比访问加权融合提高了37.4%。在代表性配置中，尽管仅在随机间歇接触下运行，EG-TDP也优于使用所有智能体每步观测的联合贝叶斯参考方法。
英文摘要
We study decentralized multi-agent target search where homogeneous agents communicate intermittently at Poisson-distributed times. Standard unconditional belief fusion wastes communication opportunities by synchronizing agents during high-entropy exploration, when diverse independent beliefs provide better coverage than a premature consensus. We introduce \emph{entropy-gated belief coordination}, in which agents skip fusion while their collective entropy ratio exceeds a threshold~\(θ\) and merge only during exploitation, consistent with the bifurcation structure of nonlinear opinion dynamics and the submodular structure of the per-step information gain objective. We further derive \(\Istep(α,β)\), the expected mutual information per observation step between two agents' binary sensors, as an interpretable, communication-free measure of sensor informativeness that motivates the gating design and guides system-level analysis. Experiments across 103{,}680 trials (nine grid sizes up to \(100{\times}100\), Poisson communication timing, four target movement patterns) show that the Entropy-Gated Trust-Decay Planner (\textsc{EG-TDP}), which adds a detection-probability planner switch in exploitation mode, achieves mean belief quality \(\bar{Q}=0.300\) (mean belief mass at the true target cell, averaged across all trials and steps), a \(58.9\%\) gain over arithmetic mean and a \(37.4\%\) gain over visit-weighted fusion. On representative configurations, EG-TDP also outperforms a joint-Bayesian reference that uses all agents'~observations at every step, despite operating under random intermittent contact only.
