Abstract
We study how two self-interested agents that play a sequence of randomly generated normal form games, each game played once, can achieve cooperation without being exploited. The agent learns if the opponent is willing to cooperate by tracking the attitude of its opponent, which tells how much the opponent values its own payoff relative to the agent’s payoff. We present experimental results obtained against different types of non-stationary opponents. The results show that a small number of games is sufficient to achieve cooperation.
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References
Au, T.C., Kraus, S., Nau, D.: Symbolic noise detection in the noisy iterated chicken game and the noisy iterated battle of the sexes. In: Proceeding of the First International Conference on Computational Cultural Dynamics, ICCCD 2007 (2007)
Axelrod, R.M.: The evolution of cooperation. Basic Books (1984)
Camerer, C.F.: Progress in behavioral game theory. The Journal of Economic Perspectives 11(4), 167–188 (1997)
Conitzer, V., Sandholm, T.: AWESOME: A general multiagent learning algorithm that converges in self-play and learns a best response against stationary opponents. Machine Learning 67(1-2), 23–43 (2007)
Crandall, J.W., Goodrich, M.A.: Learning to compete, cooperate, and compromise using reinforcement learning. Machine Learning (2010)
Damer, S., Gini, M.: Achieving cooperation in a minimally constrained environment. In: Proc. of the Nat’l Conf. on Artificial Intelligence, pp. 57–62 (2008)
Damer, S., Gini, M.: Extended abstract: Friend or foe? detecting an opponent’s attitude in normal form games. In: Proc. Int’l Conf. on Autonomous Agents and Multi-Agent Systems (2011)
Fitzgerald, B.D.: Self-interest or altruism. Journal of Conflict Resolution 19, 462–479 (1975)
Frohlich, N.: Self-Interest or Altruism, What Difference? Journal of Conflict Resolution 18(1), 55–73 (1974)
Fudenberg, D., Levine, D.K.: The Theory of Learning in Games. MIT Press (1998)
McClintock, C.G., Allison, S.T.: Social value orientation and helping behavior. Journal of Applied Social Psychology 19(4), 353–362 (1989)
Musso, C., Oudjane, N., Legland, F.: Improving regularized particle filters. In: Doucet, A., de Freitas, N., Gordon, N. (eds.) Sequential Monte Carlo Methods in Practice, pp. 247–271. Springer, New York (2001), citeseer.ist.psu.edu/musso01improving.html
Nowak, M.A.: Five rules for the evolution of cooperation. Science 314, 1560–1563 (2006)
Powers, R., Shoham, Y., Vu, T.: A general criterion and an algorithmic framework for learning in multi-agent systems. Machine Learning 67(1-2), 45–76 (2007)
Shapley, L.S.: Stochastic games. Proceedings of the NAS 39, 1095–1100 (1953)
Valavanis, S.: The resolution of conflict when utilities interact. The Journal of Conflict Resolution 2(2), 156–169 (1958), http://www.jstor.org/stable/172973
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Damer, S., Gini, M. (2013). Cooperation without Exploitation between Self-interested Agents. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33932-5_51
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DOI: https://doi.org/10.1007/978-3-642-33932-5_51
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