Skip to main content

Adaptability by Behavior Selection and Observation for Mobile Robots

  • Conference paper
Soft Computing in Engineering Design and Manufacturing
  • 377 Accesses

Abstract

Behavior-based systems are very useful in making robots adapt to the dynamics of real-world environments. To make these systems more adaptable to various situations and goals to pursue in the world, an interesting idea is to dynamically select behaviors that control the actions of the system. One factor that can give a lot of information about the world is the observation of use of behaviors (or Behavior Exploitation). Using this factor, the system can self-evaluate the proper working of its behaviors, giving it more adaptability. The paper describes how Behavior Exploitation can be used to influence motives using a simulated environment for mobile robots, and to acquire knowledge about the world using a Pioneer I mobile robot. It also exposes the repercussion on programming the system, and how it can help in designing a general control architecture for intelligent systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Brooks, R.A., 1986, A robust layered control system for a mobile robot, IEEE Journal of Robotics and Automation, RA-2(1), 14–23.

    Article  Google Scholar 

  2. Brooks, R.A., 1991, Challenges for complete creature architectures, in From Animals to Animals. Proc. First Int’l Conf. on Simulation of Adaptive Behavior, The MIT Press, pp. 434-443.

    Google Scholar 

  3. Bonasso, R.P., Kortcnkamp, D., Miller, D.P., and Slack, M., 1995, Experiences with an architecture for intelligent reactive agents, Internal Report Metrica Robotics and Automation Group, NASA Johnson Space Center.

    Google Scholar 

  4. Ferguson, I.A., 1992, Toward an architecture for adaptive, rational, mobile agents, in Decentralized A.I.-3. Proc. Third European Workshop on Modelling Autonomous Agents in a Multi-Agent World, Werner, E. and Demazeau, Y. (ed.), Elsevier Science, pp. 249-261.

    Google Scholar 

  5. Firby, R.J., 1989, Adaptive execution in complex dynamic worlds, Ph.D. Thesis, Dept. Computer Science, Yale.

    Google Scholar 

  6. Saffiotti, A., Ruspini, E., and Konolige, K., 1993, A fuzzy controller for Flakey, an autonomous mobile robot. Technical Note 529, SRI International.

    Google Scholar 

  7. Maes, P., 1991, A bottom-up mechanism for behavior selection in an artificial creature, in From Animals to Animals. Proc. First Int’l Conf. on Simulation of Adaptive Behavior, The MIT Press, pp. 238-246.

    Google Scholar 

  8. Michaud, F., 1996, Nouvelle architecture unifiée de contrôle intelligent par sélection intentionnelle de comportements, Ph.D. Thesis, Université de Sherbrooke, Department of Electrical and Computer Engineering.

    Google Scholar 

  9. Michaud, F., Lachiver, G., and Dinh, C.T.L., 1996, A new control architecture combining reactivity, deliberation and motivation for situated autonomous agent, in Proc. Fourth Int’l Conf. on Simulation of Adaptive Behavior, Cape Cod, September.

    Google Scholar 

  10. Michaud, F., Lachrver, G., and Dinh, C.T.L., 1996, Fuzzy selection and blending of behaviors for situated autonomous agent, in Proc. IEEE Int’l Conf. on Fuzzy Systems, New Orleans, September.

    Google Scholar 

  11. Dolan, S.L. and Lamoureux, G., 1990, Initiation à la Psychologic du Travail, Gaetan Morin Ed.

    Google Scholar 

  12. Almassy, N., 1993, BugWorld: A distributed environment for the development of control architectures in multiagent worlds, Tech. Report 93.32, Dept. of Computer Science, University Zurich-Irchcl.

    Google Scholar 

  13. Mataric, M.J., 1992, Integration of representation into goal-driven behavior-based robots, IEEE Trans. on Robotics and Automation,8(3), 304–312.

    Article  Google Scholar 

  14. Michaud, F. and Malaric, M.J., 1997, Behavior evaluation and learning from an internal point of view, in Proc. of FLAIRS (Florida Al International Conference), Daytona, Florida, May.

    Google Scholar 

  15. Michaud, F. and Mataric, M.J., 1997, A history-based learning approach for adaptive robot behavior selection, Tech. report CS-97-192, Computer Science Department, Brandeis University.

    Google Scholar 

  16. Ram, A. and Santamaria, J.C., 1993, Multistrategy learning in reactive control systems for autonomous robotic navigation, Informatica, 17(4), 347–369.

    Google Scholar 

  17. McCallum, A.K., 1996, Learning to use selective attention and shorl-term memory in sequential tasks, in Proc. of the Fourth Int’l Conf. on Simulation of Adaptive Behavior, Cape Cod, September, pp. 315-324.

    Google Scholar 

  18. Brooks, R.A., 1996, MARS: Multiple Agency Reactivity System, IS Robotics Documentation.

    Google Scholar 

  19. McFarland, D. and Bosser, T., 1993, Intelligent Behavior in Animals and Robots, Bradford Book, The MIT Press.

    Google Scholar 

  20. Pfeifer, R., 1995, Cognition—Perspectives from autonomous agents, Robotics and Autonom. Syst., 15, 47–70.

    Article  Google Scholar 

  21. Smithers, T., 1994, On why better robots make it harder, in From Animals to Animals 3. Proc, Third Int’l Conf. on Simulation of Adaptive Behaviors, The MIT Press, pp. 64-72.

    Google Scholar 

  22. Smithers, T., 1995, Are autonomous agents information processing systems, in The Artificial Life Route to Artificial Intelligence: Building Embodied. Situated Agents, Steels, L. and Brooks, R. (ed.), Lawrence Erlbaum Associates, chap. 4, pp. 123-162.

    Google Scholar 

  23. Steels, L., 1995, Intelligence—Dynamics and representations, in The Biology and Technology of Intelligent Autonomous Agents, Springer Verlag, Berlin.

    Chapter  Google Scholar 

  24. Antsaklis, P., 1994, Defining intelligent control. Report of the task force on intelligent control, IEEE Control Systems, 4-5 & 58-66.

    Google Scholar 

  25. Steels, L., 1995, The Homo Cyber Sapiens, the robot Homonidus Intelligens, and the ‘artificial life’ approach to artificial intelligence, in Burda Symposium on Brain-Computer Interface.

    Google Scholar 

  26. Albus, J.S., 1991, Outline for a theory of intelligence, IEEE Trans. on Syst., Man, and Cybern., 21(3), 473–509.

    Article  MathSciNet  Google Scholar 

  27. Corfield, S.J., Fraser, R.J.C., and Harris, C.J., 1991, Architecture for real-time control of autonomous vehicles, Comput. Control. Eng. J., 2(6), 254–262.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag London

About this paper

Cite this paper

Michaud, F. (1998). Adaptability by Behavior Selection and Observation for Mobile Robots. In: Chawdhry, P.K., Roy, R., Pant, R.K. (eds) Soft Computing in Engineering Design and Manufacturing. Springer, London. https://doi.org/10.1007/978-1-4471-0427-8_40

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-0427-8_40

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-76214-0

  • Online ISBN: 978-1-4471-0427-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics