A kill chain optimization method for improving the resilience of unmanned combat system-of-systems

Zhong, Yuanfu; Li, Hongxu; Sun, Qin; Huang, Zhiwen; Zhang, Yingchao

doi:10.1016/j.chaos.2024.114685

Cited by 4 publications

(1 citation statement)

References 40 publications

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“…Sun et al [10] proposed a method framework called UCSoS autonomous decision-making to enable SoS with reconfiguration capability amidst battlefield disturbances. Zhong et al [26] aimed to restructure the collaborative relationships between network nodes and optimize the selection of kill chains in the operational system, thereby enhancing its resilience to disruptions.…”

Section: Ucsos Reconstructionmentioning

confidence: 99%

Optimal Unmanned Combat System-of-Systems Reconstruction Strategy with Heterogeneous Cost via Deep Reinforcement Learning

Li,

Yuan,

Ren

et al. 2024

Mathematics

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The unmanned combat system-of-systems (UCSoS) in modern warfare is comprised of various interconnected entities that work together to support mission accomplishment. The soaring number of entities makes the UCSoS fragile and susceptible to triggering cascading effects when exposed to uncertain disturbances such as attacks or failures. Reconfiguring the UCSoS to restore its effectiveness in a self-coordinated and adaptive manner based on the battlefield situation and operational requirements has attracted increasing attention. In this paper, we focus on the UCSoS reconstruction with heterogeneous costs, where the collaboration nodes may have different reconstruction costs. Specifically, we adopt the heterogeneous network to capture the interdependencies among combat entities and propose a more representative metric to evaluate the UCSoS reconstruction effectiveness. Next, we model the combat network reconstruction problem with heterogeneous costs as a nonlinear optimization problem and prove its NP-hardness. Then, we propose an approach called SoS-Restorer, which is based on deep reinforcement learning (DRL), to address the UCSoS reconstruction problem. The results show that SoS-Restorer can quickly generate reconstruction strategies and improve the operational capabilities of the UCSoS by about 20∼60% compared to the baseline algorithm. Furthermore, even when the size of the UCSoS exceeds that of the training data, SoS-Restorer exhibits robust generalization capability and can efficiently produce satisfactory results in real time.

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Section: Ucsos Reconstructionmentioning

confidence: 99%