Petri网
计算机科学
概率逻辑
过程(计算)
代表(政治)
流程架构(architecture)
人工智能
自然语言处理
知识获取
知识表示与推理
理论计算机科学
机器学习
程序设计语言
政治
政治学
法学
作者
Hua Shi,Ya-Xuan Yu,Ran Liu,Hu‐Chen Liu
出处
期刊:IEEE Transactions on Fuzzy Systems
[Institute of Electrical and Electronics Engineers]
日期:2024-04-01
卷期号:32 (4): 2198-2210
被引量:2
标识
DOI:10.1109/tfuzz.2023.3347436
摘要
Fuzzy Petri nets (FPNs) are widely used in various fields for knowledge representation and reasoning. Nevertheless, the original FPN model has many limitations when applied in the practical situations. In response, this article aims to develop a new FPN model, named as probabilistic linguistic Petri nets (PLPNs), for representing and acquiring knowledge based on a dynamic consensus reaching process. First, to avoid losing the initial information, the probabilistic linguistic term sets (PLTSs) are adopted to represent the professional knowledge of domain experts. Subsequently, a knowledge acquisition approach is proposed to acquire the knowledge parameters of PLPNs. Then, a dynamic consensus reaching method is introduced during the knowledge acquisition process to increase the group consensus level among experts. Finally, a real-world risk assessment example concerning the subway fire system is presented to validate the usefulness and effectiveness of the proposed PLPNs. The results show that the new PLPNs are efficient and practical to represent and acquire expert knowledge with conflict and inconsistent opinions.
科研通智能强力驱动
Strongly Powered by AbleSci AI