On Performance Gauge of Average Multi-Cue Multi-Choice Decision Making: A Converse Lyapunov Approach

Mehdi Firouznia; Qing Hui

doi:10.1109/JAS.2020.1003471

Volume 8 Issue 1

Jan. 2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2021 > 8(1): 136-147

Mehdi Firouznia and Qing Hui, "On Performance Gauge of Average Multi-Cue Multi-Choice Decision Making: A Converse Lyapunov Approach," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 136-147, Jan. 2021. doi: 10.1109/JAS.2020.1003471

Citation:

Mehdi Firouznia and Qing Hui, "On Performance Gauge of Average Multi-Cue Multi-Choice Decision Making: A Converse Lyapunov Approach," IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 136-147, Jan. 2021. doi: 10.1109/JAS.2020.1003471

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On Performance Gauge of Average Multi-Cue Multi-Choice Decision Making: A Converse Lyapunov Approach

doi: 10.1109/JAS.2020.1003471

Mehdi Firouznia,
Qing Hui^,

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Author Bio:
Mehdi Firouznia received the B.Sc. and M.Sc. degrees in electrical and power engineering from Sharif University of Technology, Iran, in 2001 and 2005, respectively. He received the Ph.D. degree in electrical and computer engineering in 2019 from University of Nebraska-Lincoln, Lincoln, USA. From 2005 to 2013, he was working in mathematical modeling and optimization in the area of renewable energy. He is now a Post Doctoral Associate in the Department of Electrical and Biomedical Engineering at University of Vermont, Burlington, USA, where he works on packetized energy management system application in regulation services. His research interests include mathematical and data-driven modeling, optimization and statistical analysis

Qing Hui (S’01–M’08) received the B.Eng. degree in aerospace engineering from the National University of Defense Technology in 1999, the M.Eng. degree in automotive engineering from Tsinghua University in 2002, and the M.S. degree in applied mathematics and the Ph.D. degree in aerospace engineering from the Georgia Institute of Technology, USA, in 2005 and 2008, respectively. From 2008 to 2015 he was an Assistant Professor and then an Associate Professor in the Department of Mechanical Engineering at Texas Tech University, USA. Since August 2015, he has been with the Department of Electrical and Computer Engineering at University of Nebraska-Lincoln, USA, where he is currently an Associate Professor. His current research interests are multi-domain cyber-physical network modeling, consensus and control of cyber-physical networks, network fault detection and diagnosis, cognitive neural network modeling and analysis, human-in-the-loop modeling and control, swarm intelligence, and high performance scientific computing. He is co-author of the book Nonnegative and Compartmental Dynamical Systems (Princeton Univ. Press, 2010)
Corresponding author: Q. Hui is with the Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588 USA (e-mail: qing.hui@unl.edu)
Received Date: 2020-03-17
Revised Date: 2020-04-23
Accepted Date: 2020-06-13

Available Online: 2020-06-29

Abstract

Abstract

Motivated by the converse Lyapunov technique for investigating converse results of semistable switched systems in control theory, this paper utilizes a constructive induction method to identify a cost function for performance gauge of an average, multi-cue multi-choice (MCMC), cognitive decision making model over a switching time interval. It shows that such a constructive cost function can be evaluated through an abstract energy called Lyapunov function at initial conditions. Hence, the performance gauge problem for the average MCMC model becomes the issue of finding such a Lyapunov function, leading to a possible way for designing corresponding computational algorithms via iterative methods such as adaptive dynamic programming. In order to reach this goal, a series of technical results are presented for the construction of such a Lyapunov function and its mathematical properties are discussed in details. Finally, a major result of guaranteeing the existence of such a Lyapunov function is rigorously proved.
- Cognitive modeling,
- decision making,
- Lyapunov function,
- multi-cue multi-choice tasks,
- performance gauge

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References

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Used a novel constructive method to prove the existence of a cost function that can put the dynamic behavior of multi-cue multi-choice decision making into an optimum perspective.
Used a Lyapunov-based control-theoretic approach to study dynamic properties decision making models.
Discovered a new converse Lyapunov theorem for a class of switched linear systems.

On Performance Gauge of Average Multi-Cue Multi-Choice Decision Making: A Converse Lyapunov Approach

doi: 10.1109/JAS.2020.1003471

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