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

  • Ivan Tanev
  • Katsunori Shimohara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2801)

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.

Keywords

Social Behavior Genetic Programming Document Object Model Cooperative Environment Complex Social Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ivan Tanev
    • 1
  • Katsunori Shimohara
    • 1
    • 2
  1. 1.ATR Human Information Science LaboratoriesKyotoJapan
  2. 2.Graduate School of InformaticsKyoto UniversityKyotoJapan

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