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

Ruozhe Li, Hao Yuan, Bangbang Ren (), Xiaoxue Zhang, Tao Chen and Xueshan Luo
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Ruozhe Li: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Hao Yuan: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Bangbang Ren: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Xiaoxue Zhang: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Tao Chen: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Xueshan Luo: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China

Mathematics, 2024, vol. 12, issue 10, 1-23

Abstract: 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.

Keywords: unmanned combat system-of-systems; heterogeneous cost; optimal reconstruction strategy; deep reinforcement learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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