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Improvement of action selection in robots based on learning fuzzy cognitive map and analysis of variance: the case of soccer server simulation environment

Author

Listed:
  • Seyed Koosha Golmohammadi
  • Ali Azadeh
  • Amir Hossein Gharehgozli
  • Zeinab Raoofi

Abstract

One of the main issues in developing automatic response systems especially autonomous robots is selecting the best action among all possible actions. Fuzzy cognitive maps (FCMs) aim to mimic the reasoning process of the human. FCMs are able to capture and imitate human behaviour by describing, developing and representing models. FCMs are also popular for their simplicity and transparency while being successful in a variety of applications. We developed a novel model that could be used for action selection in robots. This model is constructed on a learning FCM which is relied on improved non-linear Hebbian algorithm. We tested our model through a series of practical experiments on the latest version of Soccer Server Simulation 3D environment. Our tests involved carefully defined factors to measure the team performance. Our results showed a significant improvement in overall performance. The significance of the proposed model was verified by analysis of variance (ANOVA).

Suggested Citation

  • Seyed Koosha Golmohammadi & Ali Azadeh & Amir Hossein Gharehgozli & Zeinab Raoofi, 2014. "Improvement of action selection in robots based on learning fuzzy cognitive map and analysis of variance: the case of soccer server simulation environment," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 16(2), pages 184-199.
    • Handle: RePEc:ids:ijisen:v:16:y:2014:i:2:p:184-199
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