Learning and adaptivity: Enhancing reactive behaviour architectures in real-world interaction systems

  • Miles Pebody
5. Robotics and Emulation of Animal Behavior

DOI: 10.1007/3-540-59496-5_335

Part of the Lecture Notes in Computer Science book series (LNCS, volume 929)
Cite this paper as:
Pebody M. (1995) Learning and adaptivity: Enhancing reactive behaviour architectures in real-world interaction systems. In: Morán F., Moreno A., Merelo J.J., Chacón P. (eds) Advances in Artificial Life. ECAL 1995. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol 929. Springer, Berlin, Heidelberg

Abstract

The success of the behaviour-based approach to designing robot control structures has largely been a result of the bottom-up development of a number of fast, tightly coupled control processes. These are specifically designed for a particular agent-environment situation. The onus is on the designers to provide the agent with a suitable response repertoire using their own knowledge of the proposed agent-environment interaction. A need for learning and adaptivity to be built into robot systems from the lowest levels is identified. An enhancement to basic reactive robot control architectures is proposed that enables processes to learn and subsequently adapt their input-output mapping. This results in both a local and global increase in robustness as well as a simplification of the design process. An implementation of the proposed mechanism is demonstrated in a real-world situated system: the control of an active laser scanning sensor.

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Copyright information

© Springer-Verlag 1995

Authors and Affiliations

  • Miles Pebody
    • 1
  1. 1.Department Of Computer ScienceUniversity College LondonLondonUK

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