Fuzzy Petri Nets for Knowledge Representation, Acquisition and Reasoning

Hua Shi received the M.S. and Ph.D. degrees in Management Science and Engineering from Shanghai University, Shanghai, China, in 2017 and 2020, respectively. He is currently a lecturer with the School of Materials, Shanghai Dianji University, Shanghai, China. He has authored or coauthored over 30 publications in international journals. His research interests include artificial intelligence, quality and reliability management, and uncertain decision-making. Hu-Chen Liu received his M.S. degree in industrial engineering from Tongji University, Shanghai, China, in 2010, and his Ph.D. degree in industrial engineering and management from Tokyo Institute of Technology, Tokyo, Japan, in 2013. He is now a distinguished professor at the School of Economics and Management, Tongji University. His main research interests include quality and reliability management, artificial intelligence, and Petri net theory and application. He has published more than 100 publications including 3 books, 90+ journal papers. 

lt;p>Chapter 1. FPNs for knowledge representation and reasoning: A literature review.- Chapter 2. Determining truth degrees of input places in FPNs.- Chapter 3. Bipolar fuzzy Petri nets for knowledge acquisition and representation.- Chapter 4. Picture fuzzy Petri nets for knowledge acquisition and representation.- Chapter 5. R-numbers Petri nets for knowledge acquisition and representation.- Chapter 6. Intuitionistic fuzzy Petri nets for knowledge representation and reasoning.- Chapter 7. Linguistic Z-number Petri nets for knowledge acquisition and representation.- Chapter 8. Linguistic reasoning Petri nets for knowledge representation and reasoning.- Chapter 9. Dynamic adaptive fuzzy Petri nets for knowledge representation and reasoning.- Chapter 10. Spherical linguistic Petri nets for knowledge representation and reasoning.- Chapter 11. Two-dimensional uncertain linguistic Petri Net for knowledge representation and reasoning.- Chapter 12. Pythagorean fuzzy Petri nets for knowledge representation and reasoning.- Chapter 13. Grey reasoning Petri nets for knowledge representation and reasoning.- Chapter 14. Cloud reasoning Petri nets for knowledge representation and reasoning.- Chapter 15. Knowledge acquisition and representation using interval-valued intuitionistic fuzzy Petri nets.- Chapter 16. Knowledge acquisition and representation using dynamic adaptive fuzzy Petri nets.- Chapter 17. Fault diagnosis and cause analysis using dynamic adaptive fuzzy Petri nets.- Chapter 18. Failure mode and effects analysis using FPNs.- Chapter 19. Failure mode and effect analysis using probabilistic linguistic Petri nets.- Chapter 20. Failure mode and effect analysis using interval type-2 fuzzy Petri nets.