YANG C, LIU H X, WANG Y, et al. Task reliability evaluation method for autonomous intelligent system based on multi-agent approach[J]. Chinese Journal of Ship Research, 2025, 20(2): 1–15 (in Chinese). DOI: 10.19693/j.issn.1673-3185.03596
Citation: YANG C, LIU H X, WANG Y, et al. Task reliability evaluation method for autonomous intelligent system based on multi-agent approach[J]. Chinese Journal of Ship Research, 2025, 20(2): 1–15 (in Chinese). DOI: 10.19693/j.issn.1673-3185.03596

Task reliability evaluation method for autonomous intelligent system based on multi-agent approach

More Information
  • Received Date: October 15, 2023
  • Revised Date: March 24, 2024
  • Available Online: March 06, 2024
© 2025 The Authors. Published by Editorial Office of Chinese Journal of Ship Research. Creative Commons License
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • Objectives 

    Aiming at the problem that the task reliability of autonomous intelligent systems is difficult to evaluate accurately due to such characteristics as the decoupling of components, changing systems, and complex fault propagation laws, this paper proposes a multi-agent-based task reliability evaluation method for autonomous intelligent systems.

    Methods 

    By constructing a reliability simulation framework based on a double-layer network structure, the separation expression and coupling correlation of the autonomous intelligent system’s information propagation process and fault propagation process are realized. By designing the attributes, behaviors, and interfaces of various components in the autonomous intelligent system, a universal fault propagation and state switching mechanism is built. Based on an analysis of the factors affecting the task reliability of the autonomous intelligent system, a multi-stage task success criterion is established. Finally, based on the Anylogic simulation platform, a task reliability evaluation model for autonomous intelligent systems is developed which is capable of single process simulation and multiple Monte Carlo simulation.

    Results 

    The simulation results show that the proposed method can quantitatively evaluate the reliability of the typical tasks of intelligent autonomous systems, as well as exploring the key fault factors affecting such reliability.

    Conclusions 

    The proposed method can realize the state perception and autonomous organization capability of autonomous intelligent systems.

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