Abstract
Convention emergence is an effective strategy to solve the coordination problem in Multiagent systems (MASs). To achieve coordination of the system, it is important to investigate how rapidly synthesize an ideal convention through repeated local interactions and learning among agents. Many methods have been proposed to generate convention, but it is difficulty to deal with the effectiveness of small-world networks and the efficiency of convergence speed. In addition, there is still the limitation of local observation of agents, which affects the learning in the MASs. This paper presents a novel learning strategy to accelerate the emergence of convention based on a Multiple-Local information table (ML-table), which integrates Local information table (L-table) collected from neighbors. Extensive agent-based simulations verify that our algorithm outperforms the advanced algorithms in terms of convergence efficiency and robustness in various networks.
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References
Ågotnes, T., Wooldridge, M.: Optimal social laws. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1, vol. 1, pp. 667–674 (2010)
Airiau, S., Sen, S., Villatoro, D.: Emergence of conventions through social learning. Auton. Agents Multi-Agent Syst. 28(5), 779–804 (2013). https://doi.org/10.1007/s10458-013-9237-x
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Foerster, J., Assael, I.A., De Freitas, N., Whiteson, S.: Learning to communicate with deep multi-agent reinforcement learning. In: Advances in Neural Information Processing Systems, pp. 2137–2145 (2016)
Hanneman, R.A., Riddle, M.: Introduction to social network methods (2005)
Hao, J., Leung, H.F.: The dynamics of reinforcement social learning in cooperative multiagent systems. In: Twenty-Third International Joint Conference on Artificial Intelligence (2013)
Hu, S., Leung, C.W., Leung, H.F., Liu, J.: To be big picture thinker or detail-oriented? Utilizing perceived gist information to achieve efficient convention emergence with bilateralism and multilateralism. In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, pp. 2021–2023 (2019)
Hu, S., Leung, H.F.: Achieving coordination in multi-agent systems by stable local conventions under community networks. In: IJCAI, pp. 4731–4737 (2017)
Mataric, M.J.: Using communication to reduce locality in distributed multiagent learning. J. Exp. Theoret. Artif. Intell. 10(3), 357–369 (1998)
Mihaylov, M., Tuyls, K., Nowé, A.: A decentralized approach for convention emergence in multi-agent systems. Auton. Agents Multi-Agent Syst. 28(5), 749–778 (2013). https://doi.org/10.1007/s10458-013-9240-2
Morales, J., Lopez-Sanchez, M., Rodriguez-Aguilar, J.A., Wooldridge, M.J., Vasconcelos, W.W.: Automated synthesis of normative systems. AAMAS 13, 483–490 (2013)
Mukherjee, P., Sen, S., Airiau, S.: Norm emergence under constrained interactions in diverse societies. In: AAMAS (2), pp. 779–786 (2008)
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006)
Savarimuthu, B.T.R., Cranefield, S.: Norm creation, spreading and emergence: a survey of simulation models of norms in multi-agent systems. Multiagent Grid Syst. 7(1), 21–54 (2011)
Sen, O., Sen, S.: Effects of social network topology and options on norm emergence. In: Padget, J., Artikis, A., Vasconcelos, W., Stathis, K., da Silva, V.T., Matson, E., Polleres, A. (eds.) COIN-2009. LNCS (LNAI), vol. 6069, pp. 211–222. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14962-7_14
Sen, S., Airiau, S.: Emergence of norms through social learning. In: IJCAI, vol. 1507, p. 1512 (2007)
Shoham, Y., Tennenholtz, M.: Robotics laboratory. In: Principles of Knowledge Representation and Reasoning: Proceedings of the Third International Conference, KR 1992, p. 225. Morgan Kaufmann Pub. (1992)
Shoham, Y., Tennenholtz, M.: Co-learning and the evolution of social activity. Stanford University California, Department of Computer Science, Technical report (1994)
Shoham, Y., Tennenholtz, M.: On the emergence of social conventions: modeling, analysis, and simulations. Artif. Intell. 94(1–2), 139–166 (1997)
Villatoro, D., Sabater-Mir, J., Sen, S.: Social instruments for robust convention emergence. In: Twenty-Second International Joint Conference on Artificial Intelligence (2011)
Villatoro, D., Sen, S., Sabater-Mir, J.: Topology and memory effect on convention emergence. In: 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, vol. 2, pp. 233–240. IEEE (2009)
Wang, Y., Lu, W., Hao, J., Wei, J., Leung, H.F.: Efficient convention emergence through decoupled reinforcement social learning with teacher-student mechanism. In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, pp. 795–803 (2018)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)
Yang, T., Meng, Z., Hao, J., Sen, S., Yu, C.: Accelerating norm emergence through hierarchical heuristic learning. In: Proceedings of the Twenty-Second European Conference on Artificial Intelligence, pp. 1344–1352 (2016)
Young, H.P.: The economics of convention. J. Econ. Perspect. 10(2), 105–122 (1996)
Yu, C., Lv, H., Ren, F., Bao, H., Hao, J.: Hierarchical learning for emergence of social norms in networked multiagent systems. In: Pfahringer, B., Renz, J. (eds.) AI 2015. LNCS (LNAI), vol. 9457, pp. 630–643. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26350-2_56
Yu, C., et al.: Modelling adaptive learning behaviours for consensus formation in human societies. Sci. Rep. 6(1), 1–13 (2016)
Yu, C., Zhang, M., Ren, F., Luo, X.: Emergence of social norms through collective learning in networked agent societies. In: Proceedings of the 2013 International Conference on Autonomous Agents and Multi-Agent Systems, pp. 475–482 (2013)
Zhang, W., Ma, L., Li, X.: Multi-agent reinforcement learning based on local communication. Cluster Comput. 22(6), 15357–15366 (2018). https://doi.org/10.1007/s10586-018-2597-x
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Chen, C., Luo, C., Chen, W. (2021). An Efficient Framework of Convention Emergence Based on Multiple-Local Information. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_3
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