← 研踪 PaperTrailarXiv:2611.04370 · v1
Distributed Marginal Return: A Communication-Efficient Algorithm for Multi-Agent Weapon Target Assignment
首次提交 2026-11-03 13:59
版本更新 2026-11-03 13:59
雷达观测 2026-11-04 23:40
摘要
The Weapon-Target Assignment (WTA) problem in distributed multi-agent systems is challenged by limited situational awareness and communication constraints. Existing distributed methods, such as auction-consensus algorithms, typically assume task-value independence, a simplification that fails to capture the coupled effects of multi-weapon assignments. This paper introduces the Distributed Marginal Return (DMR) algorithm, a novel decentralized approach that generalizes auction-consensus frameworks. DMR enables agents to compute bids based on marginal return and uses a consensus protocol to resolve conflicts, allowing multiple non-conflicting proposals to be accepted in parallel for cooperative optimization with minimal communication. A key theoretical contribution is our proof that the marginal returns of non-conflicting bids are additive, a property that ensures global consistency and validates our parallel acceptance mechanism. Extensive simulations demonstrate that DMR significantly outperforms baseline algorithms in both solution quality and communication efficiency, offering a scalable and robust solution for real-world distributed command and control applications.
作者备注：12 pages, 6 figures. Published in the IEEE Systems Journal
期刊信息：IEEE Systems Journal, 2026
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v1 · 2026-11-03 13:59 · 最新观测版本
Distributed Marginal Return: A Communication-Efficient Algorithm for Multi-Agent Weapon Target Assignment
The Weapon-Target Assignment (WTA) problem in distributed multi-agent systems is challenged by limited situational awareness and communication constraints. Existing distributed methods, such as auction-consensus algorithms, typically assume task-value independence, a simplification that fails to capture the coupled effects of multi-weapon assignments. This paper introduces the Distributed Marginal Return (DMR) algorithm, a novel decentralized approach that generalizes auction-consensus frameworks. DMR enables agents to compute bids based on marginal return and uses a consensus protocol to resolve conflicts, allowing multiple non-conflicting proposals to be accepted in parallel for cooperative optimization with minimal communication. A key theoretical contribution is our proof that the marginal returns of non-conflicting bids are additive, a property that ensures global consistency and validates our parallel acceptance mechanism. Extensive simulations demonstrate that DMR significantly outperforms baseline algorithms in both solution quality and communication efficiency, offering a scalable and robust solution for real-world distributed command and control applications.
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