A Method for Learning a Petri Net Model Based on Region Theory

Authors

  • Jiao Li The MOE Key Laboratory of Embedded System and Service Computation, Tongji University, Shanghai, 201804, China
  • Ru Yang The MOE Key Laboratory of Embedded System and Service Computation, Tongji University, Shanghai, 201804, China
  • Zhijun Ding The MOE Key Laboratory of Embedded System and Service Computation, Tongji University, Shanghai, 201804, China
  • Meiqin Pan School of Business and Management, Shanghai International Studies University, Shanghai, 200083, China

DOI:

https://doi.org/10.31577/cai_2020_1-2_174

Keywords:

Petri net, robot model, robot learning, region theory, Petri net synthesis

Abstract

The deployment of robots in real life applications is growing. For better control and analysis of robots, modeling and learning are the hot topics in the field. This paper proposes a method for learning a Petri net model from the limited attempts of robots. The method can supplement the information getting from robot system and then derive an accurate Petri net based on region theory accordingly. We take the building block world as an example to illustrate the presented method and prove the rationality of the method by two theorems. Moreover, the method described in this paper has been implemented by a program and tested on a set of examples. The results of experiments show that our algorithm is feasible and effective.

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Published

2020-02-29

How to Cite

Li, J., Yang, R., Ding, Z., & Pan, M. (2020). A Method for Learning a Petri Net Model Based on Region Theory. Computing and Informatics, 39(1-2), 174–192. https://doi.org/10.31577/cai_2020_1-2_174

Issue

Vol. 39 No. 1-2 (2020): Computing and Informatics

Section

Special Section Articles

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