The Efficacy of Choosing Strategy with General Regression Neural Network on Evolutionary Markov Games

Shirin Kordnoori, Hamidreza Mostafaei, Mohammadmohsen Ostadrahimi, Saeed Agha Banihashemi

Abstract


Nowadays, Evolutionary Game Theory which studies the learning model of players,has attracted more attention than before. These Games can simulate the real situationand dynamic during processing time. This paper creates the Evolutionary MarkovGames, which maps players’ strategy-choosing to a Markov Decision Processes(MDPs) with payoffs. Boltzmann distribution is used for transition probability andthe General Regression Neural Network (GRNN) simulating the strategy-choosing inEvolutionary Markov Games. Prisoner’s dilemma is a problem that uses the methodand output results showing the overlapping the human strategy-choosing line andGRNN strategy-choosing line after 48 iterations, and they choose the same strate-gies. Also, the error rate of the GRNN training by Tit for Tat (TFT) strategy is lowerthan similar work and shows a better res

Keywords


Boltzmann distribution; Evolutionary Game Theory; General Regression Neural Network; Neuro-Fuzzy Network; Tit for Tat Strategy

Full Text:

Full Text

References


Liu WB, Wang XJ. An evolutionary game based particle swarm optimization algorithm. Journal of Computational and Applied Mathematics 2008;214(1):30–35. https://www.sciencedirect.com/science/article/pii/S0377042707000799.

Tomandl D, Schober A. A Modified General Regression Neural Network (MGRNN) with new, efficient training algorithms as a robust ‘black box’-tool for data analysis. Neural Networks 2001;14(8):1023–1034. https://www.sciencedirect.com/science/article/pii/S089360800100051X.

Ganesan T, Elamvazuthi I, Vasant P. Multiobjective design optimization of a nano-CMOS voltage-controlled oscillator using game theoretic-differential evolution. Applied Soft Computing 2015;32:293–299. https://www.sciencedirect.com/science/article/pii/S1568494615001726.

Wood AD, Mason CF, Finnoff D. OPEC, the Seven Sisters, and oil market dominance: An evolutionary game theory and agent-based modeling approach. Journal of Economic Behavior & Organization 2016;132:66–78. https://www.sciencedirect.com/science/article/pii/S0167268116301202, thresholds, Tipping Points, and Random Events in Dynamic Economic Systems.

Sharma R. Lyapunov Theory Based Stable Markov Game Fuzzy Control for Non-Linear Systems. Eng Appl Artif Intell 2016 Oct;55(C):119–127. https://doi.org/10.1016/j.engappai.2016.06.008.

Yang G. Game Theory-Inspired Evolutionary Algorithm for Global Optimization. Algorithms 2017;10(4). https://www.mdpi.com/1999-4893/10/4/111.

Garay J, Csiszár V, Móri TF. Evolutionary stability for matrix games under time constraints. Journal of Theoretical Biology 2017;415:1–12. https://www.sciencedirect.com/science/article/pii/S002251931630409X.

Tosh D, Sengupta S, Kamhoua CA, Kwiat KA. Establishing evolutionary game models for CYBer security information EXchange (CYBEX). Journal of Computer and System Sciences 2018;98:27–52. https://www.sciencedirect.com/science/article/pii/S002200001630085X.

Quan J, Liu W, Chu Y, Wang X. Stochastic dynamics and stable equilibrium of evolutionary optional public goods game in finite populations. Physica A: Statistical Mechanics and its Applications 2018;502:123–134. https://www.sciencedirect.com/science/article/pii/S0378437118301882.

Overton CE, Broom M, Hadjichrysanthou C, Sharkey KJ. Methods for approximating stochastic evolutionary dynamics on graphs. Journal of Theoretical Biology 2019;468:45–59. https://www.sciencedirect.com/science/article/pii/S0022519319300724.

Izquierdo LR, Izquierdo SS, Sandholm WH. An introduction to ABED: Agent-based simulation of evolutionary game dynamics. Games and Economic Behavior 2019;118:434–462. https://www.sciencedirect.com/science/article/pii/S0899825619301459.

jia Wang X, ling Gu C, Quan J. Evolutionary game dynamics of the Wright-Fisher process with different selection intensities. Journal of Theoretical Biology 2019;465:17–26. https://www.sciencedirect.com/science/article/pii/S0022519319300062.

Gu C, Wang X, Zhao J, Ding R, He Q. Evolutionary game dynamics of Moran process with fuzzy payoffs and its application. Applied Mathematics and Computation 2020;378:125227. https://www.sciencedirect.com/science/article/pii/S009630032030196X.

Singh R, Dwivedi AD, Srivastava G, Wiszniewska-Matyszkiel A, Cheng X. A game theoretic analysis of resource mining in blockchain. Cluster Computing 2020 23:3 2020 jan;23(3):2035–2046. https://link.springer.com/article/10.1007/s10586-020-03046-w.

Srivastava G, Vinoth Kumar CNS, Kavitha V, Parthiban N, Venkataraman R. Two-stage data encryption using chaotic neural networks. Journal of Intelligent & Fuzzy Systems 2020 jan;38(3):2561–2568.

Weibing L, Xianjia W, Binbin H. Evolutionary Markov Games Based on Neural Network. In: Yu W, He H, Zhang N, editors. Advances in Neural Networks – ISNN 2009 Berlin, Heidelberg: Springer Berlin Heidelberg; 2009. p. 109–115




DOI: http://dx.doi.org/10.12962/j20882033.v32i1.7074

Refbacks

  • There are currently no refbacks.


Creative Commons License

IPTEK Journal of Science and Technology by Lembaga Penelitian dan Pengabdian kepada Masyarakat, ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/jts.