Nash equilibrium seeking for N-coalition noncooperative games
An N-coalition noncooperative game is formulated in this paper. In the formulated game, there are N interacting coalitions and each of them includes a set of agents. Each coalition acts as a virtual player that aims to minimize its own objective function. This objective function is defined as the su...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Online Access: | https://hdl.handle.net/10356/138758 |
| _version_ | 1826129157792202752 |
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| author | Ye, Maojiao Hu, Guoqiang Lewis, Frank L. |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Ye, Maojiao Hu, Guoqiang Lewis, Frank L. |
| author_sort | Ye, Maojiao |
| collection | NTU |
| description | An N-coalition noncooperative game is formulated in this paper. In the formulated game, there are N interacting coalitions and each of them includes a set of agents. Each coalition acts as a virtual player that aims to minimize its own objective function. This objective function is defined as the sum of the agents’ local objective functions in the coalition and is a function of all the engaged agents’ actions in the game. However, the actual decision-makers are not the coalitions but the agents therein. That is, the agents within each coalition collaboratively minimize the coalition's objective function while constituting an entity that serves as a self-interested player (i.e., the coalition) in the game among the interacting coalitions. A seeking strategy is designed for the agents to find the Nash equilibrium of the N-coalition noncooperative game. The equilibrium seeking strategy is based on an adaptation of a dynamic average consensus protocol and the gradient play. The dynamic average consensus protocol is leveraged to estimate the averaged gradients of the coalitions’ objective functions. The gradient play is then implemented by utilizing the estimated information to achieve the Nash equilibrium seeking. Convergence results are established by utilizing Lyapunov stability analysis. Numerical examples are given in supportive of the theoretical results. |
| first_indexed | 2024-10-01T07:36:07Z |
| format | Journal Article |
| id | ntu-10356/138758 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:36:07Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1387582020-05-12T07:46:23Z Nash equilibrium seeking for N-coalition noncooperative games Ye, Maojiao Hu, Guoqiang Lewis, Frank L. School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Nash Equilibrium Seeking Social Cost Minimization An N-coalition noncooperative game is formulated in this paper. In the formulated game, there are N interacting coalitions and each of them includes a set of agents. Each coalition acts as a virtual player that aims to minimize its own objective function. This objective function is defined as the sum of the agents’ local objective functions in the coalition and is a function of all the engaged agents’ actions in the game. However, the actual decision-makers are not the coalitions but the agents therein. That is, the agents within each coalition collaboratively minimize the coalition's objective function while constituting an entity that serves as a self-interested player (i.e., the coalition) in the game among the interacting coalitions. A seeking strategy is designed for the agents to find the Nash equilibrium of the N-coalition noncooperative game. The equilibrium seeking strategy is based on an adaptation of a dynamic average consensus protocol and the gradient play. The dynamic average consensus protocol is leveraged to estimate the averaged gradients of the coalitions’ objective functions. The gradient play is then implemented by utilizing the estimated information to achieve the Nash equilibrium seeking. Convergence results are established by utilizing Lyapunov stability analysis. Numerical examples are given in supportive of the theoretical results. NRF (Natl Research Foundation, S’pore) EDB (Economic Devt. Board, S’pore) 2020-05-12T07:46:23Z 2020-05-12T07:46:23Z 2018 Journal Article Ye, M., Hu, G., & Lewis, F. L. (2018). Nash equilibrium seeking for n-coalition noncooperative games. Automatica, 95, 266-272. doi:10.1016/j.automatica.2018.05.020 0005-1098 https://hdl.handle.net/10356/138758 10.1016/j.automatica.2018.05.020 2-s2.0-85047839132 95 266 272 en Automatica © 2018 Elsevier Ltd. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Nash Equilibrium Seeking Social Cost Minimization Ye, Maojiao Hu, Guoqiang Lewis, Frank L. Nash equilibrium seeking for N-coalition noncooperative games |
| title | Nash equilibrium seeking for N-coalition noncooperative games |
| title_full | Nash equilibrium seeking for N-coalition noncooperative games |
| title_fullStr | Nash equilibrium seeking for N-coalition noncooperative games |
| title_full_unstemmed | Nash equilibrium seeking for N-coalition noncooperative games |
| title_short | Nash equilibrium seeking for N-coalition noncooperative games |
| title_sort | nash equilibrium seeking for n coalition noncooperative games |
| topic | Engineering::Electrical and electronic engineering Nash Equilibrium Seeking Social Cost Minimization |
| url | https://hdl.handle.net/10356/138758 |
| work_keys_str_mv | AT yemaojiao nashequilibriumseekingforncoalitionnoncooperativegames AT huguoqiang nashequilibriumseekingforncoalitionnoncooperativegames AT lewisfrankl nashequilibriumseekingforncoalitionnoncooperativegames |