Effects of Learning to Interact on the Evolution of Social Behavior of Agents in Continuous Predators-prey Pursuit Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Tanev:2003:ECAL,

• author = "Ivan Tanev and Katsunori Shimohara",
• title = "Effects of Learning to Interact on the Evolution of Social Behavior of Agents in Continuous Predators-prey Pursuit Problem",
• booktitle = "Proceedings of the 7th European Conference on Artificial Life (ECAL 2003)",
• year = "2003",
• editor = "Wolfgang Banzhaf and Jens Ziegler and Thomas Christaller and Peter Dittrich and JanT Kim",
• volume = "2801",
• series = "Lecture Notes in Computer Science",
• pages = "138--145",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, multi-agent systems, emergence, learning",
• isbn13 = "978-3-540-20057-4",
• DOI = "doi:10.1007/978-3-540-39432-7_15",
• abstract = "the effect of learning to interact on the evolution of social behaviour of agents situated in inherently cooperative environment. Using continuous predators-prey pursuit problem we verified our hypothesis that relatively complex social behaviour 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 behaviour. 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.",

}

Genetic Programming entries for Ivan T Tanev Katsunori Shimohara

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