Entropy-Gated Belief Coordination for Decentralized Multi-Agent Search Under Intermittent Communication

2026-10-05

AI Summary

This study addresses the problem in multi-agent search under intermittent communication, where unconditional belief fusion disrupts coverage advantages during high-entropy exploration phases. To overcome this, we propose an entropy-gated coordination mechanism grounded in nonlinear opinion dynamics and submodular structure. By integrating a trust-decay planner, expected mutual information analysis, and Poisson communication modeling, the method achieves an adaptive strategy that skips fusion during exploration while merging beliefs during exploitation. Experimental results demonstrate that this mechanism improves mean belief quality by 58.9%, significantly outperforming arithmetic averaging and weighted fusion baselines, and even surpassing the full-observation Bayesian reference upper bound.

Abstract

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.

Problem

• decentralized multi-agent search
• intermittent communication
• belief fusion
• target search

Innovation

• Entropy-Gated Belief Coordination
• Decentralized Multi-Agent Search
• Intermittent Communication
• Mutual Information
• Submodular Optimization