Abstract
We consider a 2-dimensional discrete space modeled by Cellular Automata consisting of \(m \times n\) cells that can be occupied by agents. There exist several types of agents which differ in their way of behavior related to their own strategy when they interact with neighbors. We assume that interaction between agents is governed by a spatial Prisoner’s Dilemma game. Each agent participates in several games with his neighbors and his goal is to maximize his payoff using own strategy. Agents can change their strategies in time by replacing their own strategy with a more profitable one from its neighborhood. While agents act in such a way to maximize their incomes we study conditions of emerging collective behavior in such systems measured by the average total payoff of agents in the game or by an equivalent measure–the total number of cooperating players. These measures are the external criteria of the game, and players acting selfishly are not aware of them. We show experimentally that collective behavior in such systems can emerge if some conditions related to the game are fulfilled. We propose to introduce an income-sharing mechanism to the game, giving a possibility to share incomes locally by agents. We present the results of an experimental study showing that the sharing mechanism is a distributed optimization algorithm that significantly improves the capabilities of emerging collective behavior measured by the external criterion of the game.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fernández Domingos, E., et al.: Emerging cooperation in N-person iterated prisoner’s dilemma over dynamic complex networks. Comput. Inform. 36, 493–516 (2017)
Gąsior, J., Seredyński, F.: Security-aware distributed job scheduling in cloud computing systems: a game-theoretic cellular automata-based approach. In: Rodrigues, J.M.F., et al. (eds.) ICCS 2019. LNCS, vol. 11537, pp. 449–462. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22741-8_32
Katsumata, Y., Ishida, Y.: On a membrane formation in a spatio-temporally generalized prisoner’s dilemma. In: Umeo, H., Morishita, S., Nishinari, K., Komatsuzaki, T., Bandini, S. (eds.) ACRI 2008. LNCS, vol. 5191, pp. 60–66. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79992-4_8
Khaluf, Y., Ferrante, E., Simoens, P., Huepe, C.: Scale invariance in natural and artificial collective systems: a review. J. R. Soc. Interface 14, 20170662 (2017)
Nowak, M.A., May, R.M.: Evolutionary games and spatial chaos. Nature 359, 826 (1992)
Osborne, M.: An Introduction to Game Theory. Oxford Univ. Press, New York (2009)
Peleteiro, A., Burguillo, J.C., Bazzan, A.L.: Emerging cooperation in the spatial IPD with reinforcement learning and coalitions. In: Bouvry, P., González-Vélez, H., Kołodziej, J. (eds.) Intelligent Decision Systems in Large-Scale Distributed Environments. Studies in Computational Intelligence, vol. 362, pp. 187–206. Springer, Cham (2011). https://doi.org/10.1007/978-3-642-21271-0_9
Seredyński, F.: Competitive coevolutionary multi-agent systems: the application to mapping and scheduling problems. J. Parallel Distrib. Comput. 47(1), 39–57 (1997)
Gąsior, J., Seredyński, F., Hoffmann, R.: Towards self-organizing sensor networks: game-theoretic \(\epsilon \)-learning automata-based approach. In: Mauri, G., El Yacoubi, S., Dennunzio, A., Nishinari, K., Manzoni, L. (eds.) ACRI 2018. LNCS, vol. 11115, pp. 125–136. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99813-8_11
Seredyński, F., Gąsior, J., Hoffmann, R.: The second order CA-based multiagent systems with income sharing. In: Gwizdałła, T.M., Manzoni, L., Sirakoulis, G.C., Bandini, S., Podlaski, K. (eds.) Cellular Automata. ACRI 2020, vol. 12599, pp. 134–145. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-69480-7_14
Wolfram, S.: A New Kind of Science. Wolfram Media (2002)
Östberg, P., Byrne, J., et al.: Reliable capacity provisioning for distributed cloud/edge/fog computing applications. In: 2017 European Conference on Networks and Communications (EuCNC), pp. 1–6, June 2017
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Seredyński, F., Kulpa, T., Hoffmann, R. (2022). Competition and Cooperation Mechanisms for Collective Behavior in Large Multi-agent Systems. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13351. Springer, Cham. https://doi.org/10.1007/978-3-031-08754-7_65
Download citation
DOI: https://doi.org/10.1007/978-3-031-08754-7_65
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08753-0
Online ISBN: 978-3-031-08754-7
eBook Packages: Computer ScienceComputer Science (R0)