Autonomous confrontation strategy learning evolution mechanism of unmanned system group under actual combat in the loop

Wang, Zhenhua; Guo, Yan; Li, Ning; Huang, Yuan; Hu, Shiguang; Lei, B.Q.; Wei, Jianyu

doi:10.1016/j.comcom.2023.07.006

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…As a unique operational concept, mosaic warfare aims to realize the autonomous collaboration of various types of combat entities and construct an on-demand integrated, highly flexible, and adaptable system-of-systems (SoS) to meet the requirements of combat tasks [1,2]. The UCSoS concept refers to the integrated use of various homogeneous or heterogeneous unmanned combat platforms, including unmanned aircraft, unmanned vehicles, and unmanned underwater vehicles, to create a new form of distributed, networked, and collaborative combat force [3][4][5]. Under the mosaic warfare concept, the relationship between different combat entities in the UCSoS is no longer fixed but adaptively transformed based on combat tasks and evolving battlefield situations.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%