Khaw, Yan Ngee and Kowalczyk, Ryszard and Vo, Quoc Bao and Abd Rahim, Nasrudin and Che, Hang Seng (2021) Transition-state replicator dynamics. Expert Systems with Applications, 182. ISSN 0957-4174, DOI https://doi.org/10.1016/j.eswa.2021.115254.
Full text not available from this repository.Abstract
Agent-based evolutionary game theory studies the dynamics of the autonomous agents. It is important for application that relies on the agents to perform the automated tasks. Since the agents make their own decision, therefore the stability of the interaction needs to be comprehended. The current state of the art in agent-based replicator dynamics are piecewise and state-coupled replicator dynamics which focus on joint-action single-state reward. This paper introduces additional reward parameter to the learning algorithm, extends the replicator dynamics to joint-action transition-state reward and shows that it can be changed to single-state reward and independent-action reward. The replicator equation is expressed based on the tree diagram approach and is verified with the numerical simulation in a two states battle of sexes coordination game for various types of rewards. The numerical results are consistent with the phase portraits generated by the replicator equation and are able to provide some general insights to the coordination game such as the number of convergence points, the rate of convergence and the effect of initial points on the convergence.
| Item Type: | Article |
|---|---|
| Funders: | Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Australia, Universiti Malaya [PG208-2015A], UM-MOHE HIR grant [UM/HIR/MOHE/ENG/22], MyPhD under MyBrain15 of Kementerian Pendidikan Malaysia (KPM), Swinburne University of Technology, Melbourne, Australia, UM Power Energy Dedicated Advanced Centre (UMPEDAC), Higher Institution Centre of Excellence (HICoE) Program Research Grant, Ministry of Education, Malaysia [RU003-2020] |
| Uncontrolled Keywords: | Evolutionary game theory; Multi-agent learning; Replicator dynamics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Deputy Vice Chancellor (Research & Innovation) Office > UM Power Energy Dedicated Advanced Centre |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 25 Jul 2022 03:36 |
| Last Modified: | 25 Jul 2022 03:36 |
| URI: | http://eprints.um.edu.my/id/eprint/28119 |
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