Abstract
The evolution of cooperation among intelligent agents is a fundamental issue in multi-agent systems. It is well accepted that the individual strategy-updating rules play a significant role in the cooperation dynamics on graphs. The imitation mechanisms account for a large proportion of these rules, in which an individual will choose a neighbor with higher payoff and follows its strategy. In this paper, we propose a strategy-updating rule based on incremental learning process for continuous prisoner’s dilemma game. Under our strategy-updating rule, each individual refreshes its decision according to original strategy (self-opinion) and new strategy learnt from one of neighbors (social-opinion). The simulation results show the incremental learning rule can enhance cooperation dramatically when individual has higher resistance to imitate others or lower payoff sensitivity. We also find that the incremental learning rule has greater influence when individual obtains fewer information of neighbors’ payoff. The reason behind the phenomena is also given. Our results may shed some light on how cooperative strategies are actually adopted and spread in spatial network.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Luke S, Sullivan K, Panait L, Balan G (2005) Tunably decentralized algorithms for cooperative target observation. In Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems. ACM, pp 911–917
Gheorghe M, Holcombe M, Kefalas P (2001) Computational models of collective foraging. BioSystems 61(2):133–141
Camorlinga S, Barker K, Anderson J (2004) Multiagent systems for resource allocation in peer-to-peer systems. In Proceedings of the winter international symposium on Information and communication technologies. Trinity College, Dublin, pp 1–6
Tanimoto J (2007) A study on a difference of dynamics between discrete and continuous strategies of a multi player game having linear payoff structure. Ipsj J 48(7):2372–2376
Axelrod R (1984) The evolution of cooperation. Basic books, New York
Szabó G, Fath G (2007) Evolutionary games on graphs. Phys Rep 446(4):97–216
Nowak MA (2006) Five rules for the evolution of cooperation. Science 314(5805):1560–1563
Du J, Wu B, Altrock PM, Wang L (2014) Aspiration dynamics of multi-player games in finite populations. J R Soc Interface 11(94):20140077
Bendor J, Swistak P (1995) Types of evolutionary stability and the problem of cooperation. Proc Natl Acad Sci 92(8):3596–3600
Szolnoki A, Perc M, Danku Z (2008) Making new connections towards cooperation in the prisoner’s dilemma game. EPL (Europhys Lett) 84(5):50007
Macy MW, Flache A (2002) Learning dynamics in social dilemmas. Proc Natl Acad Sci 99(suppl 3):7229–7236
Marchiori D, Warglien M (2008) Predicting human interactive learning by regret-driven neural networks. Science 319(5866):1111–1113
Traulsen A, Semmann D, Sommerfeld RD, Krambeck HJ, Milinski M (2010) Human strategy updating in evolutionary games. Proc Natl Acad Sci 107(7):2962–2966
Zhong W, Kokubo S, Tanimoto J (2012) How is the equilibrium of continuous strategy game different from that of discrete strategy game? BioSystems 107(2):88–94
Acknowledgements
This work is partly supported by National Natural Science Foundation of China under grant No. 61300087, No. 61702076 and No. 61502069; “the Fundamental Research Funds for the Central Universities” under grant No. DUT17RW131.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhao, X., Xu, Z., Han, X., Tian, L., Xu, X. (2018). Evolution of Cooperation in Spatial Prisoner’s Dilemma Game Based on Incremental Learning. In: Deng, Z. (eds) Proceedings of 2017 Chinese Intelligent Automation Conference. CIAC 2017. Lecture Notes in Electrical Engineering, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-10-6445-6_6
Download citation
DOI: https://doi.org/10.1007/978-981-10-6445-6_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6444-9
Online ISBN: 978-981-10-6445-6
eBook Packages: EngineeringEngineering (R0)