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Preferences, Machine Learning, and Decision Support With Cyber-Physical Systems

Preferences, Machine Learning, and Decision Support With Cyber-Physical Systems

Yuri P. Pavlov, Evgeniy Ivanov Marinov
Copyright: © 2018 |Pages: 32
ISBN13: 9781522555100|ISBN10: 1522555102|EISBN13: 9781522555117
DOI: 10.4018/978-1-5225-5510-0.ch011
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MLA

Pavlov, Yuri P., and Evgeniy Ivanov Marinov. "Preferences, Machine Learning, and Decision Support With Cyber-Physical Systems." Cyber-Physical Systems for Next-Generation Networks, edited by Joel J. P. C. Rodrigues and Amjad Gawanmeh, IGI Global, 2018, pp. 222-253. https://doi.org/10.4018/978-1-5225-5510-0.ch011

APA

Pavlov, Y. P. & Marinov, E. I. (2018). Preferences, Machine Learning, and Decision Support With Cyber-Physical Systems. In J. Rodrigues & A. Gawanmeh (Eds.), Cyber-Physical Systems for Next-Generation Networks (pp. 222-253). IGI Global. https://doi.org/10.4018/978-1-5225-5510-0.ch011

Chicago

Pavlov, Yuri P., and Evgeniy Ivanov Marinov. "Preferences, Machine Learning, and Decision Support With Cyber-Physical Systems." In Cyber-Physical Systems for Next-Generation Networks, edited by Joel J. P. C. Rodrigues and Amjad Gawanmeh, 222-253. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5510-0.ch011

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Abstract

Modeling of complex processes with human participations causes difficulties due to the lack of precise measurement coming from the qualitative nature of the human notions. This provokes the need of utilization of empirical knowledge expressed cardinally. An approach for solution of these problems is utility theory. As cyber-physical systems are integrations of computation, networking, and physical processes in interaction with the user is needed feedback loops, the aim of the chapter is to demonstrate the possibility to describe quantitatively complex processes with human participation. This approach permits analytical representations of the users' preferences as objective utility functions and modeling of the complex system “human-process.” The mathematical technique allows CPS users dialog and is demonstrated by two case studies, portfolio allocation, and modeling of a competitive trade by a finite game and utility preference representation of the trader. The presented formulations could serve as foundation of development of decision support tools and decision control.

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