Abstract
The evolution from individual to collective learning opens a new dimension of solutions to address problems that appeal for gradual adaptation in dynamic and unpredictable environments. A team of individuals has the potential to outperform any sum of isolated efforts, and that potential is materialized when a good system of interaction is considered. In this paper, we describe two forms of cooperation that allow multi-agent learning: the sharing of partial results obtained during the learning activity, and the social adaptation to the stages of collective learning. We consider different ways of sharing information and different options for social reconfiguration, and apply them to the same learning problem. The results show the effects of cooperation and help to put in perspective important properties of the collective learning activity.
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References
Aamodt, A., Plaza, E.: Case based reasoning: foundational issues, methodological variations, and system approaches. In: Artificial Intelligence Communications (AICom), vol. 7, pp. 39–59. IOS Press, Amsterdam (1994)
Chalkiadakis, G., Boutilier, C.: Coordination in multiagent reinforcement learning: a bayesian approach. In: AAMAS 2003. Proceedings of the 2nd Conference on Autonomous Agents and Multiagent Systems (2003)
Clouse, J.A.: Learning from an automated training agent. In: Proceedings of the International Machine Learning Conference (IMLC) (1995)
Graça, P.R., Gaspar, G.: Using cognition and learning to improve agents’ reactions. In: Alonso, E., Kudenko, D., Kazakov, D. (eds.) Adaptive Agents and Multi-Agent Systems. LNCS (LNAI), vol. 2636, pp. 239–259. Springer, Heidelberg (2003)
Kazakov, D., Kudenko, D.: Machine learning and inductive logic programming for multi-agent systems. In: Luck, M., Mařík, V., Štěpánková, O., Trappl, R. (eds.) ACAI 2001 and EASSS 2001. LNCS (LNAI), vol. 2086, pp. 246–270. Springer, Heidelberg (2001)
Modi, P.J., Shen, W.: Collaborative multiagent learning for classification tasks. In: Agents 2001. Proceedings of the 5th Conference on Autonomous Agents (2001)
Nunes, L., Oliveira, E.: Cooperative learning using advice exchange. In: Alonso, E., Kudenko, D., Kazakov, D. (eds.) Adaptive Agents and Multi-Agent Systems. LNCS (LNAI), vol. 2636, pp. 33–48. Springer, Heidelberg (2003)
Ontañon, S., Plaza, E.: A bartering approach to improve multiagent learning. In: Alonso, E., Kudenko, D., Kazakov, D. (eds.) Adaptive Agents and Multi-Agent Systems. LNCS (LNAI), vol. 2636, Springer, Heidelberg (2003)
Szer, D., Charpillet, F.: Coordination through mutual notification in cooperative multiagent reinforcement learning. In: Kudenko, D., Kazakov, D., Alonso, E. (eds.) Adaptive Agents and Multi-Agent Systems II. LNCS (LNAI), vol. 3394, Springer, Heidelberg (2005)
Tan, M.: Multi agent reinforcement learning: independent vs cooperative agents. In: Proceedings of the 10th International Conference on Machine Learning, Amherst, MA, pp. 330–337 (1993)
Vu, T., Powers, R., Shoham, Y.: Learning against multiple opponents. In: AAMAS 2006. Proceedings of the 5th Conference on Autonomous Agents and Multiagent Systems (2006)
Weiβ, G., Dillenbourg, P.: What is ‘multi’ in multi-agent learning. In: Dillenbourg, P. (ed.) Collaborative Learning, pp. 64–80. Pergamon Press, Oxford (1999)
Whitehead, S.D.: A complexity analysis of cooperative mechanisms in reinforcement learning. In: AAAI 1991. Proceedings of the 9th National Conference on Artificial Intelligence, pp. 607–613 (1991)
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Rafael, P., Neto, J.P. (2007). Multi-agent Learning: How to Interact to Improve Collective Results. In: Neves, J., Santos, M.F., Machado, J.M. (eds) Progress in Artificial Intelligence. EPIA 2007. Lecture Notes in Computer Science(), vol 4874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77002-2_48
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DOI: https://doi.org/10.1007/978-3-540-77002-2_48
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