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

Mohamed Abdelnaby; Kevin Leahy

cs.ROOct 5, 2026

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

Authors: Mohamed Abdelnaby, Kevin Leahy

Organizations: Department of Robotics Engineering, Worcester Polytechnic Institute

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.

Figures & tables

Fig. 1: Decision overview for the three proposed coordination policies. At each communication event the team computes

H_{\mathrm{ratio}}

, the entropy gate determines whether to merge and, for EG-TDP and EG-Istep, which planner objective to use.

Fig. 2:

I_{\mathrm{step}}(\alpha,\beta)

over the binary sensor parameter space. Higher values indicate faster belief divergence during blackouts, making coordination more valuable. The star marks our sensor profile (

\alpha{=}0.1,\beta{=}0.2

I_{\mathrm{step}}{=}0.114

bits).

Fig. 3: Trust-decay factor

\gamma

as a function of blackout length

k

for (

N{=}2

) agents. The complement

1{-}\gamma

goes to entropy-reduction.

Fig. 4: Overall

\bar{Q}

per method (95% CI, 12,960 trials per method). VW-KL-R is shown for completeness, results are nearly identical to EG-KL and it is omitted from Table I .

Ablations: TD-KL $\bar{Q}{=}0.021$ , TD-KL-A $\bar{Q}{=}0.033$ (Sec. VI-H ).
Method	$\bar{Q}\;\pm\;\sigma$	Succ.	Steps
Arith	$0.189\pm 0.278$	$83.0\%$	$431$
Geom	$0.256\pm 0.405$	$79.2\%$	$389$
VW-KL	$0.218\pm 0.296$	$86.2\%$	$430$
EG-KL	$0.225\pm 0.298$	$86.8\%$	$434$
EG-TDP	$\mathbf{0.300}\pm 0.401$	$\mathbf{88.2\%}$	$\mathbf{317}$

TABLE I: Overall

\bar{Q}

and discovery statistics (

\alpha{=}0.1

\beta{=}0.2

, Poisson comm., 12,960 trials per method). Steps on successful trials only.

Fig. 5:

\bar{Q}

vs grid size. EG-KL (red solid line) leads at

10{\times}10

but falls to

\bar{Q}{\approx}0.003

100{\times}100

. EG-TDP (purple solid line) maintains

\bar{Q}{=}0.218

at the largest scale, validating the planner switch. Geom degrades gracefully but at the cost of the lowest success rate.

Fig. 6: Success rate (left) and steps to discovery on successful trials (right). EG-TDP achieves the highest success rate and fastest discovery simultaneously. Geom finds the target quickly when it succeeds (389 steps) but fails most often (20.8% failure rate). All pairwise success-rate differences are statistically significant at

p{<}0.001

(two-proportion

z

-test, Bonferroni-corrected) except VW-KL vs EG-KL (

p{=}0.158

)

Fig. 7: Coordination dynamics (

\theta{=}0.6

, aggregated over patterns and

\bar{k}

). (a) Expected merges per trial (baselines always merge, EG-TDP fires only

\sim

3.6 of 156 events at

100{\times}100

, a 97% bandwidth reduction). (b) First exploitation step, annotations: % of trials never crossing

H_{\mathrm{ratio}}<\theta

. (c) Fraction of steps in exploitation (for ungated methods, we show percentages of when entropy

<\theta

Method	Stationary	Patrol	Evasive	Random
EG-TDP	0.954 (100%)	0.226 (81%)	0.016 (80%)	0.003 (93%)
EG-KL	0.579 (96%)	0.224 (82%)	0.095 (80%)	0.003 (89%)
Geom	0.926 (76%)	0.077 (74%)	0.018 (75%)	0.003 (91%)
VW-KL	0.563 (96%)	0.213 (80%)	0.094 (79%)	0.003 (90%)

TABLE II:

\bar{Q}

and success rate by target movement pattern.

$\theta$	$\bar{k}{=}5$	$\bar{k}{=}25$	$\bar{k}{=}100$
0.4	0.460	0.477	0.469
0.5	0.518	0.529	0.530
0.6	0.594	0.599	0.586
0.7	0.602	0.602	0.596
0.8	0.602	0.602	0.596

TABLE III: EG-TDP mean

\bar{Q}

by entropy threshold

\theta

and expected communication interval

\bar{k}

Method	$\bar{Q}\;\pm\;\sigma$	Succ.	Steps
Arith	$0.313\pm 0.284$	$89.7\%$	$517$
VW-KL	$0.396\pm 0.299$	$91.1\%$	$421$
EG-KL	$0.412\pm 0.294$	$92.6\%$	$434$
EG-Istep	$0.543\pm 0.379$	$92.9\%$	$319$
Full-Comm (reference)	$0.552\pm 0.318$	$95.4\%$	$323$
EG-TDP	$\mathbf{0.593}\pm 0.395$	$\mathbf{93.8\%}$	$\mathbf{296}$

TABLE IV: Proposed method comparison on representative configurations (stationary + patrol, three grid sizes, 20 trials,

\theta{=}0.6

Explore similar work

The Cost of Consensus: Malignant Epistemic Herding and Adaptive Gating in Distributed Multi-Agent Search
May 7, 2026David Farr, Iain Cruickshank, Kate Starbird +1Opinion Dynamics Multi-Agent Systems
1*Information School, University of Washington, 1410 NE Campus Parkway, Seattle, 98195, WA, USA. · 2Computer Science, Carnegie Mellon University, 5000 Forbes Avenue,2026 Pittsburgh, 15213, PA, USA. · 3Human Centered Design Engineering, University of Washington, 1410 NE Campus Parkway, Seattle, 98195, WA, USA. +1
Exact Fusion and Coordinated Exploration in Multi-Robot Active Inference
Sep 15, 2026Peng Wu, Mohsen Imani, Amidu Kamara +3Active Inference Multi-Robot Exploration
Northeastern University, Boston, MA, USA. · University of California, Irvine, CA, USA. · U.S. Department of Homeland Security, Washington, DC, USA. +1
Pathwise Information Certificates for Decentralized Adaptive Sensing
Oct 7, 2026Theodoros TsiligkaridisActive Sensing Cooperative Localization
MIT Lincoln Laboratory

May 7, 2026, cs.MAJ/K move · Enter open · S save

The Cost of Consensus: Malignant Epistemic Herding and Adaptive Gating in Distributed Multi-Agent Search

David Farr, Iain Cruickshank, Kate Starbird, Jevin West

1*Information School, University of Washington, 1410 NE Campus Parkway, Seattle, 98195, WA, USA. · 2Computer Science, Carnegie Mellon University, 5000 Forbes Avenue,2026 Pittsburgh, 15213, PA, USA. · 3Human Centered Design Engineering, University of Washington, 1410 NE Campus Parkway, Seattle, 98195, WA, USA.+1

Opinion Dynamics Multi-Agent Systems Multi-Agent Consensus Multi-Agent Communication

Open paper AskPDF arXiv

Distributed agents in real-world settings frequently must coordinate under uncertainty with only partial observations. Coordination is necessary to share beliefs to aid in task completion, but communication costs bandwidth, introduces latency, and if done poorly, can degrade collective reasoning. This tension is especially acute in bandwidth-constrained deployments such as distributed sensing networks, autonomous reconnaissance, and collaborative cyber defense, where excessive transmission carries direct operational costs. Existing work has focused on multi-agent exploration and communication strategies, but not on how communication frequency and content jointly shape the collective belief state. Central to this challenge is the degree to which agents maintain compatible internal beliefs about the environment, a property we term \textit{epistemic alignment}. When agents share beliefs effectively, they converge on correct hypotheses; when communication is poorly designed, agents may converge confidently on wrong ones. We formalize this distinction and show it is not detectable from coordination metrics alone such as Jensen-Shannon Divergence or rate to consensus.

Abstract

Explore similar work

The Cost of Consensus: Malignant Epistemic Herding and Adaptive Gating in Distributed Multi-Agent Search

Exact Fusion and Coordinated Exploration in Multi-Robot Active Inference

Pathwise Information Certificates for Decentralized Adaptive Sensing