Codevs: An extension of devs for integration of simulation and machine learning

B. S. Kim, T. G. Kim, S. H. Choi

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


When we model a system to analyse it, there are two main methods we can use. First, there are knowledge-based simulation modelling methods using system operations, such as discrete event system specification (DEVS). Conversely, there are data-driven modelling methods using data analysis without explicit system knowledge, such as machine learning. These two models can be used appropriately in situations where it is difficult to model sufficiently with one method, and through this, the advantages of each method can be maximised. In other words, for this, a method is required to specify one system by using two methods at the same time. Therefore, in this paper, we introduce an extension of DEVS formalism, called Cooperative DEVS (CoDEVS), which enables representation of both a simulation model and a machine learning model. It consists of a simulation model, data model, and interface models that convert events between the simulation and data models. We also introduce a modified simulation algorithm that can interpret the new formalism and simulate a distributed file system to show the validity of the proposed work.

Original languageEnglish
Pages (from-to)661-671
Number of pages11
JournalInternational Journal of Simulation Modelling
Issue number4
StatePublished - Dec 2021

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) funded by the Korea Government (Ministry of Science and ICT) under Grant 2021R1G1A100355911.

Publisher Copyright:
© 2021, DAAAM International Vienna. All rights reserved.


  • Cooperative DEVS (CoDEVS)
  • Data Modelling
  • DEVS Formalism
  • Machine Learning
  • Simulation Modelling


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