Markov Decision Processes Based Optimal Control Policies for Probabilistic Boolean Networks

Date

2004-06-01

Author

Abul, Osman
Alhajj, Reda
Polat, Faruk

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This paper addresses the control formulation process for probabilistic boolean genetic networks. It is a major problem that has not been investigated enough yet. We argue that a monitoring stage is necessary after the control stage for providing guidance about the evolution of the investigated state. For this purpose, we developed methods for generating optimal control policies for each of the following five cases: finite control, infinite control, finite control-infinite monitoring, finite control-finite monitoring, and repeated finite control-finite monitoring. Our initial proposal was based on using action cost functions in the process. In this study, we propose Markov decision processes as an alternative to the action cost functions approach. We conducted experiments on two simple illustrative examples to demonstrate that the considered five cases are necessary, effective and really matter while developing optimal control policies; the obtained results are promising.

Subject Keywords

Optimal control, Monitoring , Cost function, Computer science, Computer networks, Genetic engineering, Process control, Proposals , Bayesian methods, Differential equations

URI

https://hdl.handle.net/11511/73948
https://ieeexplore.ieee.org/document/1317363

DOI

https://doi.org/10.1109/BIBE.2004.1317363

Conference Name

BIBE'04 - 4th IEEE Symposium on Bioinformatics and Bioengineering (2004)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

O. Abul, R. Alhajj, and F. Polat, “Markov Decision Processes Based Optimal Control Policies for Probabilistic Boolean Networks,” presented at the BIBE′04 - 4th IEEE Symposium on Bioinformatics and Bioengineering (2004), 2004, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/73948.