Abstract
We present the results of our work on the effect of learning to interact on the evolution of social behavior of agents situated in inherently cooperative environment. Using continuous predators-prey pursuit problem we verified our hypothesis that relatively complex social behavior may emerge from simple, implicit, locally defined, and therefore – robust and highly-scalable interactions between the predator agents. We argue that the ability of agents to learn to perform simple, atomic acts of implicit interaction facilitates the performance of evolution of more complex, social behavior. The empirical results show about two-fold decrease of computational effort of proposed strongly typed genetic programming (STGP), used as an algorithmic paradigm to evolve the social behavior of the agents, when STGP is combined with learning of agents to implicitly interact with each other.
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Tanev, I., Shimohara, K. (2003). Effects of Learning to Interact on the Evolution of Social Behavior of Agents in Continuous Predators-Prey Pursuit Problem. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds) Advances in Artificial Life. ECAL 2003. Lecture Notes in Computer Science(), vol 2801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39432-7_15
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DOI: https://doi.org/10.1007/978-3-540-39432-7_15
Publisher Name: Springer, Berlin, Heidelberg
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