A perspective on Petri Net learning  

作　　者：Hongda QI Changjun JIANG 

机构地区：[1]Key Laboratory of Embedded System and Service Computing(Ministry of Education),Tongji University,Shanghai 201804,China [2]National(Province-Ministry Joint)Collaborative Innovation Center for Financial Network Security,Tongji University,Shanghai 201804,China

出　　处：《Frontiers of Computer Science》2023年第6期1-3,共3页中国计算机科学前沿（英文版）

基　　金：supported in part by the ShanghaiScience and Technology Innovation Action Plan Project(22511100700).

摘　　要：Petri Nets(PNs)are used for modeling and analyzing discreteevent systems,such as communication protocols,trafficsystems,human-computer interaction,and fault diagnosis.PNs’state space explosion problem means that the state spaceof PNs grows exponentially with PNs’size.Even thefundamental reachability problem is still an NP-Hard problemin general.It has been proved that the equivalence problem forthe reachability sets of arbitrary PNs is undecidable except forsome subclass of PNs[1].That is,the reachability problem ofarbitrary PNs cannot be solved exactly.Nowadays,there is noefficient and accurate algorithm to solve the problem.10172In recent years,with the emergence of big data and thedevelopment of computing hardware,a series ofbreakthroughs have been achieved in machine learning,suchas AlphaGo,AlphaFold,and ChatGPT[2−4].As a data-drivenapproach,machine learning can learn potential mappingrelationships between inputs and outputs from large-scaledata.

关 键 词：BREAKTHROUGH HARDWARE LEARNING 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]