An Effective Robotic Model of Action Selection

  • Fernando M. Montes González
  • Antonio Marín Hernández
  • Homero Ríos Figueroa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4177)


In this paper we present a concise analysis of the requirements for effective action selection, and a centralized action selection model that fulfills most of these requirements. In this model, action selection occurs by combining sensory information from the non-homogenous sensors of an off-the-shelf robot with the feedback from competing behavioral modules. In order to successfully clean an arena, the animal robot (animat) has to present a coherent overall behavior pattern for both appropriate selection and termination of a selected behavior type. In the same way, an animat set in a chasing task has to present opportunist action selection to locate the nearest target. In consequence, both an appropriate switching of behavior patterns and a coherent overall behavior pattern are necessary for effective action selection.


Behavior Pattern Behavior Type Action Selection Sensor Fusion Perceptual Variable 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tyrrell, T.: Computational Mechanisms for action selection, in Centre for Cognitive Science, University of Edinburgh (1993)Google Scholar
  2. 2.
    Montes Gonzalez, F., Marin Hernandez, A.: Central Action Selection using Sensor Fusion. In: The 5th Mexican International Conference on Computer Science (ENC 2004), IEEE Computer Society, Colima, México (2004)Google Scholar
  3. 3.
    Montes Gonzalez, F., Marin Hernandez, A.: The Use of Frontal and Peripheral Perception in a Prey-Catching System. In: The 4th International Symposium on Robotics and Automation (ISRA 2004), Querétaro, México (2004)Google Scholar
  4. 4.
    Montes Gonzalez, F., Flandes Eusebio, D.: The Development of a Basic Follow-Behavior within a Distributed Framework. In: The 1st IEEE Latin American Robotics Symposium (LARS 2004). México, D.F., México (2004)Google Scholar
  5. 5.
    Prescott, T.J., Redgrave, P., Gurney, K.N.: Layered control architectures in robots and vertebrates. Adaptive Behavior 7(1), 99–127 (1999)CrossRefGoogle Scholar
  6. 6.
    Snaith, S., Holland, O.: An investigation of two mediation strategies suitable for behavioural control in animals and animats. In: From Animals to Animats: Proceedings of the First International Conference Simulation of Adaptive Behaviour, Paris (1990)Google Scholar
  7. 7.
    Werner, M.G.: Using Second Order Neural Connections for Motivation of Behavioral Choices. In: Cliff, D. (ed.) From Animals to Animats 3: Proceedings of the 6th International Conference on the Simulation of Adaptive Behavior, MIT Press, Cambridge (1994)Google Scholar
  8. 8.
    Redgrave, P., Prescott, T., Gurney, K.N.: The basal ganglia: A vertebrate solution to the selection problem? Neuroscience 89, 1009–1023 (1999b)CrossRefGoogle Scholar
  9. 9.
    Ludlow, A.R.: The behaviour of a model animal. Behaviour 58, 131–172 (1976)CrossRefGoogle Scholar
  10. 10.
    Brooks, R.A.: A robust layered control system for a mobile robot. IEEE Journal on Robotics and Automation, RA-2, 14–23 (1986)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Rosenblatt, K.J., Payton, D.W.: A Fine-Grained Alternative to the Subsumption architecture for Mobile Robot Control. In: Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (1989)Google Scholar
  12. 12.
    McFarland, D.J.: Flow graph representation of motivational systems. British Journal of Mathematical and Statistical Psychology 18(1), 25–43 (1965)Google Scholar
  13. 13.
    McFarland, D.J.: Feedback Mechanisms in Animal Behaviour. Academic Press, London (1971)Google Scholar
  14. 14.
    Rosenblatt, J.K., Thorpe, C.E.: Combining multiple goals in a behavior-based architecture. In: Proceedings of 1995 International Conference on Intelligent Robots and Systems (1995)Google Scholar
  15. 15.
    Humphrys, S.: Action selection methods using reinforcement learning. In: From Animals to Animats 4: Fourth International Conference on Simulation of Adaptive Behavior (1996)Google Scholar
  16. 16.
    Montes Gonzalez, F., Prescott, T.J., Gurney, K., et al.: An embodied model of action selection mechanisms in the vertebrate brain. In: Meyer, J.A. (ed.) From Animals to Animats 6: Proceedings of the 6th International Conference on the Simulation of Adaptive Behavior, MIT Press, Cambridge (2000)Google Scholar
  17. 17.
    Prescott, T.J., Montes Gonzalez, F.M., Gurney, K., et al.: A robot model of the basal ganglia: behavior and intrinsic processing. Neural Networks (in press)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fernando M. Montes González
    • 1
  • Antonio Marín Hernández
    • 1
  • Homero Ríos Figueroa
    • 1
  1. 1.Facultad de Física e Inteligencia ArtificialUniversidad VeracruzanaVeracruzMéxico

Personalised recommendations