A perspective on Petri Net learning
Hongda QI, Changjun JIANG
A perspective on Petri Net learning
Hongda Qi received his BS and MS degrees in computer science and technology from Shandong University of Science and Technology, China in 2015 and 2018, respectively. He is currently pursuing the PhD degree from the College of Electronic and Information Engineering, Tongji University, China. His research interests include Petri net theory and machine learning
Changjun Jiang received the PhD degree from the Institute of Automation, Chinese Academy of Sciences, China in 1995. He is currently the leader of the Key Laboratory of Embedded System and Service Computing (Ministry of Education), Tongji University, China. He is an academician of Chinese Academy of Engineering, China and an IET Fellow and an Honorary Professor with Brunel University London, UK. He has been the recipient of one international prize and seven prizes in the field of science and technology
[1] |
Hack M . The equality problem for vector addition systems is undecidable. Theoretical Computer Science, 1976, 2( 1): 77–95
|
[2] |
Silver D, Huang A, Maddison C J, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D . Mastering the game of Go with deep neural networks and tree search. Nature, 2016, 529( 7587): 484–489
|
[3] |
Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl S A A, Ballard A J, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior A W, Kavukcuoglu K, Kohli P, Hassabis D . Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596( 7873): 583–589
|
[4] |
Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C L, Mishkin P, Zhang C, Agarwal S, Slama K, Ray A, Schulman J, Hilton J, Kelton F, Miller L, Simens M, Askell A, Welinder P, Christiano P, Leike J, Lowe R. Training language models to follow instructions with human feedback. In: Proceedings of the 36th Conference on Neural Information Processing Systems. 2022, 27730−27744
|
[5] |
Wang S, Zhou M, Gan M, You D, Li Y. New reachability trees for unbounded Petri nets. In: Proceedings of 2015 IEEE International Conference on Robotics and Automation. 2015, 3862−3867
|
[6] |
Wang J, Qi H, Guang M, Zhang C, Yan C, Jiang C . Net learning. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33( 12): 7380–7389
|
[7] |
Qi H, Guang M, Wang J, Yan C, Jiang C. Probabilistic reachability prediction of unbounded Petri nets: a machine learning method. IEEE Transactions on Automation Science and Engineering, 2023, doi:
|
[8] |
Guang M, Yan C, Wang J, Qi H, Jiang C. Benchmark datasets for stochastic Petri net learning. In: Proceedings of 2021 International Joint Conference on Neural Networks. 2021, 1−8
|
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