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18 November 2024 Optimized finite state machine design for autonomous decision-making of VR intelligent agents in nuclear emergency response training
Ming Guo, Fengying Hu, Zijian Xu, Biao Li, Xiaofei Feng, Guobo Zhong
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Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134032U (2024) https://doi.org/10.1117/12.3051790
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China
Abstract
In modern emergency management and training, Virtual Reality (VR) technology has become an indispensable tool due to its safety and high simulation capabilities. However, in complex scenarios such as nuclear emergency response, traditional VR systems often require the collaboration of multiple participants, increasing the complexity of organization and implementation. This paper proposes an optimized Finite State Machine (FSM) method based on state aggregation and hierarchical design to enhance the autonomous decision-making capability and task execution efficiency of VR intelligent agents. By decomposing complex tasks into different levels and aggregating similar states, the optimized FSM effectively reduces the number of states and transitions, significantly improving the system's response speed and flexibility. This provides a new solution for intelligent training systems in emergency management. The experimental results demonstrate the optimized FSM's superior performance in state reduction and transition efficiency, offering significant improvements in system response and flexibility for VR intelligent agents in nuclear emergency scenarios.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Ming Guo, Fengying Hu, Zijian Xu, Biao Li, Xiaofei Feng, and Guobo Zhong "Optimized finite state machine design for autonomous decision-making of VR intelligent agents in nuclear emergency response training", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134032U (18 November 2024); https://doi.org/10.1117/12.3051790
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KEYWORDS
Virtual reality
Decision making
Education and training
Design
Emergency preparedness
Equipment
Environmental sensing
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