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References

  1. Bai, S., Jia, X., Cheng, Z. and Guo, B. (2021). Operation strategy optimization for on-orbit satellite subsystems considering multiple active switching, Reliability Engineering and System Safety 215: 107765, DOI: 10.1016/j.ress.2021.107765.
  2. Hook, J., El-Sedky, S., Silva, V.D. and Kondoz, A.M. (2021). Learning data-driven decision-making policies in multi-agent environments for autonomous systems, Cognitive Systems Research 65(2): 40–49, DOI: 10.1016/j.cogsys.2020.09.006.
  3. Kanazawa, A., Kinugawa, J., and Kosuge, K. (2021). Motion planning for human-robot collaboration using an objective-switching strategy, IEEE Transactions on Human-Machine Systems 51(6): 590–600, DOI: 10.1109/THMS.2021.3112953.
  4. Landgren, P., Srivastava, V. and Leonard, N.E. (2021). Distributed cooperative decision making in multi-agent multi-armed bandits, Automatica 125: 109445, DOI: 10.1016/j.automatica.2020.109445.
  5. Li, Y., Ma, D., An, Z., Wang, Z., Zhong, Y., Chen, S. and Feng, C. (2022). V2x-sim: Multi-agent collaborative perception dataset and benchmark for autonomous driving, IEEE Robotics and Automation Letters 7(4): 10914–10921, DOI: 10.1109/LRA.2022.3192802.
  6. Lin, J., Su, W., Xiao, L. and Jiang, X. (2018). Adaptive modulation switching strategy based on Q-learning for underwater acoustic communication channel, International Conference on Underwater Networks & Systems, WUWNet 2018, Shenzhen, China, pp. 38:1–38:5.
  7. Liu, G., Wu, S., Zhu, L., Wang, J. and Lv, Q. (2022). Fast and smooth trajectory planning for a class of linear systems based on parameter and constraint reduction, International Journal of Applied Mathematics and Computer Science 32(1): 11–21, DOI: 10.34768/amcs-2022-0002.
  8. Ma, J., Cheng, Z., Zhang, X., Mamun, A.A., de Silva, C.W. and Lee, T.H. (2020). Data-driven predictive control for multi-agent decision making with chance constraints, arXiv: abs/2011.03213.
  9. Ming, Y., Chen, J., Dong, Y. and Wang, Z. (2022). Evolutionary game based strategy selection for hybrid v2v communications, IEEE Transactions on Vehicular Technology 71(2): 2128–2133, DOI: 10.1109/TVT.2021.3132025.
  10. Nimmolrat, A., Sutham, K. and Thinnukool, O. (2021). Patient triage system for supporting the operation of dispatch centres and rescue teams, BMC Medical Informatics and Decision Making 21(1): 68–84, DOI: 10.1186/s12911-021-01440-x.
  11. Oguz-Ekim, P., Bostanci, B., Tekkk, S.. and Synmez, E. (2020). The EKF based localization and initialization algorithms with UWB and odometry for indoor applications and ROS ecosystem, International Conference on Advanced Computing and Applications, SIU 2020, Gaziantep, Turkey, pp. 1–4.
  12. Roy, D., Chowdhury, A., Maitra, M. and Bhattacharya, S. (2018). Multi-robot virtual structure switching and formation changing strategy in an unknown occluded environment, IEEE Conference on Computer Communications, IROS 2018, Madrid, Spain, pp. 4854–4861.
  13. Sengupta, A. and Yasser Mohammad, S.N. (2021). An autonomous negotiating agent framework with reinforcement learning based strategies and adaptive strategy switching mechanism, arXiv: abs/2102.03588.
  14. Shin, M.E., Kang, T. and Kim, S. (2018). Blackboard architecture for detecting and notifying failures for component-based unmanned systems, Journal of Intelligent and Robotic Systems 90(2): 571–585, DOI: 10.1007/s10846-017-0677-4.
  15. Sun, K. and Liu, X. (2021). Path planning for an autonomous underwater vehicle in a cluttered underwater environment based on the heat method, International Journal of Applied Mathematics and Computer Science 31(2): 289–301, DOI: 10.34768/amcs-2021-0020.
  16. Tan, J., Khalili, R., Karl, H. and Hecker, A. (2022). Multi-agent distributed reinforcement learning for making decentralized offloading decisions, IEEE Conference on Computer Communications, INFOCOM 2022, London, UK, pp. 2098–2107.
  17. Wang, H., Yan, J., Han, S. and Liu, Y. (2020a). Switching strategy of the low wind speed wind turbine based on real-time wind process prediction for the integration of wind power and EVS, Renewable Energy 157: 256–272, DOI: 10.1016/j.renene.2020.04.132.
  18. Wang, P., Yang, J., Jin, Y. and Wang, J. (2020b). Research on allocation and dispatching strategies of rescue vehicles in emergency situation on the freeway, International Conference on Control, Automation, Robotics and Vision, ICARCV 2020, Shenzhen, China, pp. 130–135.
  19. Wang, X., Fu, R. and Zhang, R. (2020c). MONE: Mutation oriented norm evolution, IEEE Access 8: 205386–205395, DOI: 10.1109/ACCESS.2020.3037798.
  20. Wu, P., Chu, F., Che, A. and Zhou, M. (2018). Bi-objective scheduling of fire engines for fighting forest fires: New optimization approaches, IEEE Transactions on Intelligent Transportation Systems 19(4): 1140–1151, DOI: 10.1109/TITS.2017.2717188.
  21. Zhao, T., Zhang, W., Zhao, H. and Jin, Z. (2017). A reinforcement learning-based framework for the generation and evolution of adaptation rules, International Conference on Autonomic Computing, ICAC 2017, Columbus, USA,pp. 103–112.
  22. Zhou, Z. (2021). Large-Scale Multi-Agent Decision-Making Using Mean Field Game Theory and Reinforcement Learning, PhD thesis, University of Nevada, Reno.
DOI: https://doi.org/10.34768/amcs-2023-0040 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 553 - 568
Submitted on: Dec 14, 2022
Accepted on: Apr 20, 2023
Published on: Dec 21, 2023
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2023 Xianchang Wang, Bingyu Lv, kaiyu Wang, Rui Zhang, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.