REFERENCES
1. Zhang C, Song D, Huang C, Swami A, Chawla NV., Heterogeneous graph neural network., In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Anchorage, AK, USA: ACM; 2019. pp. 793–803.
2. Fu X, Zhang J, Meng Z, King I., MAGNN: Metapath aggregated graph neural network for heterogeneous graph embedding., In: WWW '20: The Web Conference 2020. Taipei, Taiwan: ACM/IW3C2; 2020. pp. 2331–41.
3. Jing Y, Yang Y, Wang X, Song M, Tao D., Amalgamating knowledge from heterogeneous graph neural networks., In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Online: Computer Vision Foundation/IEEE; 2021. pp. 15709–18.
4. Zhao J, Wang X, Shi C, et al., Heterogeneous graph structure learning for graph neural networks., In: Proceedings of the 35th AAAI Conference on Artificial Intelligence. Online: AAAI Press; 2021. pp. 4697–705.
5. Liu Z, Chen C, Yang X, et al., Heterogeneous gGraph neural networks for malicious account detection., In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. Torino, Italy: ACM; 2018. pp. 2077–85.
6. Jin D, Huo C, Liang C, Yang L., Heterogeneous graph neural network via attribute completion., In: WWW '21: The Web Conference 2021. Online: ACM/IW3C2; 2021. pp. 391–400.
7. Guo J, Yang C. Learning power allocation for multi-cell-multi-user systems with heterogeneous graph neural networks. IEEE Trans Wireless Commun 2021;21:884-97.
8. Bhatta K, Chang Q. An integrated control strategy for simultaneous robot assignment, tool change and preventive maintenance scheduling using Heterogeneous Graph Neural Network. Robot Comput Int Manuf 2023;84:102594.
9. Ru J, Hao D, Zhang X, Xu H, Jia Z. Research on a hybrid neural network task assignment algorithm for solving multi-constraint heterogeneous autonomous underwater robot swarms. Front Neurorobot 2023;16:1055056.
10. Zhang X, Zhao H, Xiong J, et al., Scalable Power control/beamforming in heterogeneous wireless networks with graph neural networks., In: Proceedings of the IEEE Global Communications Conference. Madrid, Spain: IEEE; 2021. pp. 1–6.
11. Wei Y, Fu X, Sun Q, et al., Heterogeneous graph neural network for privacy-preserving recommendation., In: Proceedings of the IEEE International Conference on Data Mining. Orlando, FL, USA: IEEE; 2022. pp. 528–37.
12. Barbosa VC, Huang HK., Static task allocation in heterogeneous distributed systems., COPPE, Universidade Federal do Rio de Janeiro, ES-149/88 (June 1988) 1988
13. Wang Z, Liu C, Gombolay M. Heterogeneous graph attention networks for scalable multi-robot scheduling with temporospatial constraints. Auton Robot 2022;46:249-68.
14. Zeng Q. Characteristic analysis and route optimization of heterogeneous neural network in logistics allocation system. Computationa Int Neuroscience 2022;2022:1713183.
15. Salcedo-Sanz S, Xu Y, Yao X. Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems. Comput Operat Res 2006;33:820-35.
16. Liu Y, Wang W, Hu Y, et al., Multi-agent game abstraction via graph attention neural network., In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. New York, NY, USA: AAAI Press; 2020. pp. 7211–18.
17. Yang S, Yang B, Kang Z, Deng L. IHG-MA:Inductive heterogeneous graph multi-agent reinforcement learning for multi-intersection traffic signal control. Neural Netw 2021;139:265-77.
18. Zheng P, Xia L, Li C, Li X, Liu B. Towards Self-X cognitive manufacturing network:An industrial knowledge graph-based multi-agent reinforcement learning approach. J Manuf Syst 2021;61:16-26.
19. Wang J, Yuan M, Li Y, Zhao Z. Hierarchical attention master-slave for heterogeneous multi-agent reinforcement learning. Neural Netw 2023;162:359-68.
20. Wang X, Zhang L, Lin T, et al. Solving job scheduling problems in a resource preemption environment with multi-agent reinforcement learning. Robot Comput-Int Manuf 2022;77:102324.
21. Bettini M, Shankar A, Prorok A., Heterogeneous multi-robot reinforcement learning., arXiv preprint arXiv: 230107137 2023.
22. Lee ES, Zhou L, Ribeiro A, Kumar V. Graph neural networks for decentralized multi-agent perimeter defense. Front Control Eng 2023;4:1104745.
23. Fernando M, Senanayake R, Choi H, Swany M., Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility., arXiv preprint arXiv: 230207337 2023.
24. Huang J, Su J, Chang Q. Graph neural network and multi-agent reinforcement learning for machine-process-system integrated control to optimize production yield. J Manuf Syst 2022;64:81-93.
25. Mo X, Huang Z, Xing Y, Lv C. Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network. IEEE Trans Intell Transport Syst 2022;23:9554-67.
26. Jia X, Wu P, Chen L, et al. Hdgt:Heterogeneous driving graph transformer for multi-agent trajectory prediction via scene encoding. IEEE Trans Pattern Anal Mach Intell 2023;45:13860-75.
27. Deka A, Sycara KP., Natural emergence of heterogeneous strategies in artificially intelligent competitive teams., In: Proceedings of the 12th International Conference on Swarm Intelligence. vol. 12689. Qingdao, China: Springer; 2021. pp. 13–25.
28. Ji X, Li H, Pan Z, Gao X, Tu C., Decentralized, unlabeled multi-agent navigation in obstacle-rich environments using graph neural networks., In: Proceedings of the {IEEE/RSJ} International Conference on Intelligent Robots and Systems. Prague, Czech Republic: IEEE; 2021. pp. 8936–43.
29. Li M, Chen S, Shen Y, et al. Online multi-agent forecasting with interpretable collaborative graph neural networks. IEEE Trans Neural Netw Learn Syst 2022:1-15.
30. Ezugwu AE, Frincu ME, Adewumi AO, Buhari SM, Junaidu SB. Neural network-based multi-agent approach for scheduling in distributed systems. Concurrency and Computation:Practice and Experience 2017;29:e3887.
