Action selection in continuous state and action spaces by cooperation and competition of extended kohonen maps

ABSTRACT

This paper presents an action selection framework based on an assemblage of self-organizing neural networks called Cooperative Extended Kohonen Maps. This framework encapsulates two features that significantly enhance a robot's action selection capability: self-organization in the continuous state and action spaces to provide smooth, efficient and fine motion control; action selection via the cooperation and competition of Extended Kohonen Maps to achieve more complex motion tasks. Qualitative tests demonstrate the capability of our action selection method for both single- and multi-robot motion tasks.

REFERENCES

• 1: Teuvo Kohonen, Self-organizing maps, Springer-Verlag New York, Inc., Secaucus, NJ, 1997
• 2: Kian Hsiang Low , Wee Kheng Leow , Marcelo H. Ang, Jr., A hybrid mobile robot architecture with integrated planning and control, Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1, July 15-19, 2002, Bologna, Italy [doi>10.1145/544741.544797]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Connectionism and neural nets
      I.2.9 Robotics

General Terms:
Algorithms, Design, Experimentation, Performance

Keywords:
action selection, motion control, neural networks