Data Analytics of an Information System Based on a Markov Decision Process and a Partially Observable Markov Decision Process

Authors

  • Lidong Wang Institute for Systems Engineering Research, Mississippi State University, Vicksburg, MS 39180, USA
  • Reed L. Mosher Institute for Systems Engineering Research, Mississippi State University, Vicksburg, MS 39180, USA
  • Terril C. Falls Institute for Systems Engineering Research, Mississippi State University, Vicksburg, MS 39180, USA
  • Patti Duett Institute for Systems Engineering Research, Mississippi State University, Vicksburg, MS 39180, USA

DOI:

https://doi.org/10.30564/jcsr.v5i1.5434

Abstract

Data analytics of an information system is conducted based on a Markov decision process (MDP) and a partially observable Markov decision process (POMDP) in this paper. Data analytics over a finite planning horizon and an infinite planning horizon for a discounted MDP is performed, respectively. Value iteration (VI), policy iteration (PI), and Q-learning are utilized in the data analytics for a discounted MDP over an infinite planning horizon to evaluate the validity of the MDP model. The optimal policy to minimize the total expected cost of states of the information system is obtained based on the MDP. In the analytics for a discounted POMDP over an infinite planning horizon of the information system, the effects of various parameters on the total expected cost of the information system are studied.

Keywords:

Predictive modelling, Information system, MDP, POMDP, Cybersecurity, Q-learning

References

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How to Cite

Wang, L., Mosher, R. L., Falls, T. C., & Duett, P. (2023). Data Analytics of an Information System Based on a Markov Decision Process and a Partially Observable Markov Decision Process. Journal of Computer Science Research, 5(1), 21–30. https://doi.org/10.30564/jcsr.v5i1.5434

Issue

Vol. 5 , Iss. 1 (January 2023)

Article Type

Article

License

Copyright © 2023 Lidong Wang, Reed L. Mosher, Terril C. Falls, Patti Duett

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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