Autonomous Teaming and Task Collaboration for Multi-Agent Systems in Dynamic Environments

WANG Chen; ZHU Cheng; LEI Hongtao

doi:10.11999/JEIT250079

Volume 47 Issue 8

Aug. 2025

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WANG Chen, ZHU Cheng, LEI Hongtao. Autonomous Teaming and Task Collaboration for Multi-Agent Systems in Dynamic Environments[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2883-2894. doi: 10.11999/JEIT250079

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WANG Chen, ZHU Cheng, LEI Hongtao. Autonomous Teaming and Task Collaboration for Multi-Agent Systems in Dynamic Environments[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2883-2894. doi: 10.11999/JEIT250079

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Autonomous Teaming and Task Collaboration for Multi-Agent Systems in Dynamic Environments

doi: 10.11999/JEIT250079 cstr: 32379.14.JEIT250079

1.
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
2.
National Key Laboratory of Information System Engineering, National University of Defense Technology, Changsha 410073, China

Received Date: 2025-02-12
Rev Recd Date: 2025-06-28

Available Online: 2025-07-07

Publish Date: 2025-08-27

Abstract

Abstract

Objective In dynamic and volatile battlefield environments, where the command structure of combat units may be disrupted, combat units must autonomously form appropriate tactical groups in edge operational settings, determine group affiliation, and rapidly allocate tasks. This study proposes a combat unit aggregation and planning method based on an adaptive clustering contract network, addressing the real-time limitations of traditional centralized optimization algorithms. The proposed method enables collaborative decision-making for autonomous group formation and supports multi-task optimization and allocation under dynamic battlefield conditions. Methods (1) An adaptive combat group division algorithm based on the second-order relative change rate is proposed. The optimal number of groups is determined using the Sum of Squared Errors (SSE) indicator, and spatial clustering of combat units is performed via an improved K-means algorithm. (2) A dual-layer contract network architecture is designed. In the first layer, combat groups participate in bidding by computing the net effectiveness of tasks, incorporating attributes such as attack, defense, and value. In the second layer, individual combat units conduct bidding with a load balancing factor to optimize task selection. (3) Mechanisms for task redistribution and exchange are introduced, improving global utility through a secondary bidding process that reallocates unassigned tasks and replaces those with negative effectiveness. Results and Discussions (1) The adaptive combat group division algorithm demonstrates enhanced situational awareness (Algorithm 1). Through dynamic clustering analysis, it accurately captures the spatial aggregation of combat units (Fig. 6 and Fig. 9), showing greater adaptability to environmental variability than conventional fixed-group models. (2) The multi-layer contract network architecture exhibits marked advantages in complex task allocation. The group-level pre-screening mechanism significantly reduces computational overhead, while the unit-level negotiation process improves resource utilization by incorporating load balancing. (3) The dynamic task optimization mechanism enables continuous refinement of the allocation scheme. It resolves unassigned tasks and enhances overall system effectiveness through intelligent task exchanges. Comparative experiments confirm that the proposed framework outperforms traditional approaches in task coverage and resource utilization efficiency (Table 4 and Table 5), supporting its robustness in dynamic battlefield conditions. Conclusions This study integrates clustering analysis with contract network protocols to establish an intelligent task allocation framework suited to dynamic battlefield conditions. By implementing dual-layer optimization in combat group division and task assignment, the approach improves combat resource utilization and shortens the kill chain. Future research will focus on validating the framework in multi-domain collaborative combat scenarios, refining bidding strategies informed by combat knowledge, and advancing command and control technologies toward autonomous coordination.
- Command and control,
- Contract network,
- Task assignment,
- Firepower planning,
- Combat group clustering

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References(27)

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