Abstract
In this paper an artificial immune system approach is used to model an agent that plays the Iterated Prisoner’s Dilemma. The learning process during the game is accomplished in two phases: recognition of the opponent’s strategy and selection of the best response. Each phase is carried out using an immune network. Learning abilities of the agent are analyzed, as well as its secondary response and generalization capability. Experimental results show that the immune approach achieved on-line learning; the agent also exhibited robust behavior since it was able to adapt to different environments.
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Alonso, O.M., Nino, F., Velez, M. (2004). A Robust Immune Based Approach to the Iterated Prisoner’s Dilemma. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds) Artificial Immune Systems. ICARIS 2004. Lecture Notes in Computer Science, vol 3239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30220-9_24
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DOI: https://doi.org/10.1007/978-3-540-30220-9_24
Publisher Name: Springer, Berlin, Heidelberg
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