Skip to main content

Advertisement

Springer Nature Link
Log in

Meta-path-guided causal inference for hierarchical feature alignment and policy optimization in enhancing resilience of UWSoS

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Unmanned weapon system-of-systems (UWSoS) require advanced resilience mechanisms to sustain mission-critical performance under adversarial conditions. This paper presents the meta-path-guided causal inference (MPGCI) framework, which seamlessly integrates meta-path-based feature extraction, causal disentanglement, and reinforcement learning to significantly enhance UWSoS resilience. The meta-path pool enables efficient neighbor sampling, effectively capturing complex higher-order network dependencies, while a directed acyclic graph aligns features with underlying causal structures, mitigating the effects of semantic drift. Furthermore, an actor-critic network, constrained by meta-paths, optimizes task allocation and recovery strategies in dynamic, evolving environments. By embedding meta-paths across the framework, MPGCI ensures precise feature alignment and adaptive resilience. Experimental results demonstrate MPGCI’s superior performance compared to state-of-the-art methods, highlighting its robustness in maintaining operational stability and facilitating rapid recovery in complex scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Algorithm 2
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Uday P, Chandrahasa R, Marais K (2019) System importance measures: definitions and application to system-of-systems analysis. Reliab Eng Syst Saf 191:106582

    Article  MATH  Google Scholar 

  2. Ruiyang L, Ming H, Hongyue H, Zhixue W, Cheng Y (2021) A branch and price algorithm for the robust wsos scheduling problem. J Syst Eng Electron 32(3):658–667

    Article  MATH  Google Scholar 

  3. Sun Y, Fang Z (2020) Research on projection gray target model based on fanp-qfd for weapon system of systems capability evaluation. IEEE Syst J 15(3):4126–4136

    Article  MathSciNet  MATH  Google Scholar 

  4. Wang J, Wang K (2022) Bert-based semi-supervised domain adaptation for disastrous classification. Multimedia Syst 28(6):2237–2246

    Article  MATH  Google Scholar 

  5. Wang W, Li X, Yang S, Huang M, Wang T, Duan T (2021) A design method of dynamic adaption mechanism for intelligent multi-unmanned-cluster combat system-of-systems. Syst Eng Theory Pract 41:1096–1106

    MATH  Google Scholar 

  6. Wang J, Wang K, Yao Y, Zhao H (2023) Event detection with multi-order edge-aware graph convolution networks. Data Knowl Eng 143:102109

    Article  MATH  Google Scholar 

  7. Clark B, Patt D, Schramm H (2020) Mosaic warfare: exploiting artificial intelligence and autonomous systems to implement decision-centric operations. Center for Strategic and Budgetary Assessments

  8. Zhang T, Song A, Lan Y (2020) Adaptive structure modeling and prediction for swarm unmanned system. Chin. Sci. Inf. Sci 50(1):347–362

    MATH  Google Scholar 

  9. Zhu X, Zhu X, Yan R, Peng R (2021) Optimal routing, aborting and hitting strategies of uavs executing hitting the targets considering the defense range of targets. Reliab Eng Syst Saf 215:107811

    Article  MATH  Google Scholar 

  10. Madni AM, Sievers M, Erwin D (2019) Formal and probabilistic modeling in design of resilient systems and system-of-systems. In: AIAA Scitech 2019 Forum, p 0223

  11. Chen Z, Jiao J, De X, Fan D (2022) Tradeoff optimization technology of effectiveness-cost for satellite-based on caiv method. J Sens 2022(1):2888846

    Google Scholar 

  12. Li Z, Dou Y, Xia B, Yang K, Li M (2022) System portfolio selection based on gra method under hesitant fuzzy environment. J Syst Eng Electron 33(1):120–133

    Article  MATH  Google Scholar 

  13. Jia N, Yang Z, Yang K (2019) Operational effectiveness evaluation of the swarming uavs combat system based on a system dynamics model. IEEE Access 7:25209–25224

    Article  MATH  Google Scholar 

  14. Holling CS, et al (1973) Resilience and stability of ecological systems

  15. Varajão J, Fernandes G, Amaral A, Gonçalves AM (2021) Team resilience model: an empirical examination of information systems projects. Reliab Eng Syst Saf 206:107303

    Article  Google Scholar 

  16. Sweya LN, Wilkinson S, Kassenga G (2021) A social resilience measurement tool for tanzania’s water supply systems. Int J Disaster Risk Reduct 65:102558

    Article  Google Scholar 

  17. Eldosouky A, Saad W, Mandayam N (2021) Resilient critical infrastructure: bayesian network analysis and contract-based optimization. Reliab Eng Syst Saf 205:107243

    Article  MATH  Google Scholar 

  18. Liu X, Fang Y-P, Zio E (2021) A hierarchical resilience enhancement framework for interdependent critical infrastructures. Reliab Eng Syst Saf 215:107868

    Article  MATH  Google Scholar 

  19. Chen Z, Zhao T, Jiao J, Chu J (2022) Performance-threshold-based resilience analysis of system of systems by considering dynamic reconfiguration. Proc Inst Mech Eng Part B J Eng Manuf 236(14):1828–1838

    Article  MATH  Google Scholar 

  20. Zhang J, Lv H, Hou J (2023) A novel general model for rap and rrap optimization of k-out-of-n: G systems with mixed redundancy strategy. Reliab Eng Syst Saf 229:108843

    Article  MATH  Google Scholar 

  21. Peiravi A, Nourelfath M, Zanjani MK (2022) Universal redundancy strategy for system reliability optimization. Reliab Eng Syst Saf 225:108576

    Article  MATH  Google Scholar 

  22. Levitin G, Xing L, Dai Y (2023) Optimizing partial component activation policy in multi-attempt missions. Reliab Eng Syst Saf 235:109251

    Article  MATH  Google Scholar 

  23. Iannacone L, Sharma N, Tabandeh A, Gardoni P (2022) Modeling time-varying reliability and resilience of deteriorating infrastructure. Reliab Eng Syst Saf 217:108074

    Article  MATH  Google Scholar 

  24. Poulin C, Kane MB (2021) Infrastructure resilience curves: performance measures and summary metrics. Reliab Eng Syst Saf 216:107926

    Article  MATH  Google Scholar 

  25. Liu W, Song Z (2020) Review of studies on the resilience of urban critical infrastructure networks. Reliab Eng Syst Saf 193:106617

    Article  MATH  Google Scholar 

  26. Wang K, Gou Y, Xue D, Liu J, Qi W, Hou G, Li B (2024) Resilience augmentation in unmanned weapon systems via multi-layer attention graph convolutional neural networks. Comput Mater Contin 80(2):1–10

    MATH  Google Scholar 

  27. Tran HT, Balchanos M, Domerçant JC, Mavris DN (2017) A framework for the quantitative assessment of performance-based system resilience. Reliab Eng Syst Saf 158:73–84

    Article  Google Scholar 

  28. Sun Q, Li H, Zhong Y, Ren K, Zhang Y (2024) Deep reinforcement learning-based resilience enhancement strategy of unmanned weapon system-of-systems under inevitable interferences. Reliab Eng Syst Saf 242:109749

    Article  MATH  Google Scholar 

  29. Xu M, Ouyang M, Hong L, Mao Z, Xu X (2022) Resilience-driven repair sequencing decision under uncertainty for critical infrastructure systems. Reliab Eng Syst Saf 221:108378

    Article  MATH  Google Scholar 

  30. Chen Z, Hong D, Cui W, Xue W, Wang Y, Zhong J (2023) Resilience evaluation and optimal design for weapon system of systems with dynamic reconfiguration. Reliab Eng Syst Saf 237:109409

    Article  MATH  Google Scholar 

  31. Zhang C, Chen R, Wang S, Dui H, Zhang Y (2022) Resilience efficiency importance measure for the selection of a component maintenance strategy to improve system performance recovery. Reliab Eng Syst Saf 217:108070

    Article  MATH  Google Scholar 

  32. Qiang F, Xingshuo H, Bo S, Yi R, Zili W, Dezhen Y, Yaolong H, Ronggen F (2022) Resilience optimization for multi-uav formation reconfiguration via enhanced pigeon-inspired optimization. Chin J Aeronaut 35(1):110–123

    Article  Google Scholar 

  33. Tran HT, Domerçant JC, Mavris DN (2016) A network-based cost comparison of resilient and robust system-of-systems. Procedia Comput Sci 95:126–133

    Article  MATH  Google Scholar 

  34. Peng Y, Liu C, Liu S, Liu Y, Wu Y (2022) Smarttro: optimizing topology robustness for internet of things via deep reinforcement learning with graph convolutional networks. Comput Netw 218:109385

    Article  MATH  Google Scholar 

  35. Dong T, Zhuang Z, Qi Q, Wang J, Sun H, Yu FR, Sun T, Zhou C, Liao J (2021) Intelligent joint network slicing and routing via gcn-powered multi-task deep reinforcement learning. IEEE Trans Cogn Commun Netw 8(2):1269–1286

    Article  MATH  Google Scholar 

  36. Zhao D, Tan Y, Li J, Dou Y, Li L, Liu J (2019) Research on structural robustness of weapon system-of-systems based on heterogeneous network. Syst Eng Theory Pract 39(12):3197–3207

    MATH  Google Scholar 

  37. Yang M, Liu F, Chen Z, Shen X, Hao J, Wang J (2020) Causalvae: structured causal disentanglement in variational autoencoder. arXiv preprint arXiv:2004.08697

  38. Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017) Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347

  39. Sun Q, Li H, Wang Y, Zhang Y (2022) Multi-swarm-based cooperative reconfiguration model for resilient unmanned weapon system-of-systems. Reliab Eng Syst Saf 222:108426

    Article  Google Scholar 

  40. Zhong Y, Li H, Sun Q, Huang Z, Zhang Y (2024) A kill chain optimization method for improving the resilience of unmanned combat system-of-systems. Chaos Solitons Fractals 181:114685

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the Ph.D. Intelligent Innovation Foundation Project, China (201-CXCY-A01-08-17-03), the Key Research and Development Program of Shaanxi Province (2024GX-YBXM-010), and the National Science Foundation of China (61972302).

Author information

Authors and Affiliations

  1. Northwest Institute of Mechanical and Electrical Engineering, Xianyang, 712099, China

    Kexin Wang, Dingrui Xue, Yingdong Gou, Wanlong Qi, Bo Li, Jiancheng Liu & Yinglong Feng

  2. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Dingrui Xue & Yuqing Lin

  3. School of Information Engineering, Chang’an University, Xi’an, 710071, China

    Dingrui Xue & Yuqing Lin

  4. School of Information Engineering, National University of Defense Technology, Changsha, 410003, China

    Bo Li & Jiancheng Liu

  5. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710071, China

    Yingdong Gou

  6. School of Electronics and Telecommunications, Technical University of Munich, Munich, Germany

    Yinglong Feng

Authors
  1. Kexin Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Dingrui Xue
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yingdong Gou
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Wanlong Qi
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Bo Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Jiancheng Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Yinglong Feng
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Yuqing Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding authors

Correspondence to Dingrui Xue or Yuqing Lin.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, K., Xue, D., Gou, Y. et al. Meta-path-guided causal inference for hierarchical feature alignment and policy optimization in enhancing resilience of UWSoS. J Supercomput 81, 358 (2025). https://doi.org/10.1007/s11227-024-06848-6

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-024-06848-6

Keywords