Skip to main content
Log in

RTCS: a Reactive with Tags Classifier System

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

In this work, a new Classifier System is proposed (CS). The system, a Reactive with Tags Classifier System (RTCS), is able to take into account environmental situations in intermediate decisions. CSs are special production systems, where conditions and actions are codified in order to learn new rules by means of Genetic Algorithms (GA). The RTCS has been designed to generate sequences of actions like the traditional classifier systems, but RTCS also has the capability of chaining rules among different time instants and reacting to new environmental situations, considering the last environmental situation to take a decision. In addition to the capability to react and generate sequences of actions, the design of a new rule codification allows the evolution of groups of specialized rules. This new codification is based on the inclusion of several bits, named tags, in conditions and actions, which evolve by means of GA. RTCS has been tested in robotic navigation. Results show the suitability of this approximation to the navigation problem and the coherence of tag values in rules classification.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Booker, L. Goldberg, D. E., and Holland, J. H.: Classifier systems and genetic algorithms, Artificial Intelligence 40 (1989), 235-282.

    Google Scholar 

  2. Brooks, R. A.: Intelligence without representation, Artificial Intelligence 47 (1991), 139-159.

    Article  Google Scholar 

  3. Dorigo, M.: ALECSYS and the autonoMouse: Learning to control a real robot by distributed classifier systems, Machine Learning 19 (1995), 209-240.

    Google Scholar 

  4. Golberg, D. E.: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, 1989.

    Google Scholar 

  5. González, A. J. and Dankel, D. D.: The Engineering of Knowledge-Based Systems, Prentice-Hall, Englewood Cliffs, NJ, 1993.

    Google Scholar 

  6. Grefenstette, J. J.: Credit assignment in rule discovery systems based on genetic algorithms, Machine Learning 3 (1988), 225-245.

    Google Scholar 

  7. Holland, J.: Adaptation in Natural an Artificial Systems, Univ. of Michigan Press, Ann Arbor, 1975.

    Google Scholar 

  8. Holland, J.: Adaptive algorithms for discovering and using general patterns in growing knowledge bases, Internat. J. Policy Analysis Inform. Systems 4 (1980), 245-268.

    Google Scholar 

  9. Holland, J.: Properties of the Bucket Brigade, in: Proc. of Internat. Conf. on Genetic Algorithms and Their Applications, Vol. 1, 1985, pp. 1-7.

    Google Scholar 

  10. Holland, J.: A mathematical framework for studying learning in classifier systems, Physica D 22 (1986), 307-317.

    Google Scholar 

  11. Holland, J.: Properties of the Bucket Brigade, in: J. J. Grefenstette (ed.), Proc. of an Internat. Conf. on Genetic Algorithms and Their Applications, 1986.

  12. Holland, J. H.: Hidden Order: How Adaptation Builds Complexity, Addison-Wesley, Reading, MA, 1995.

    Google Scholar 

  13. Isasi, P., Berlanga, A., Molina, J. M., and Sanchis, A.: Robot controller against environment, a competitive evolution, in: 15th IMACS World Congress 1997 on Scientific Computation, Modelling and Applied Mathematics, Special Session on Evolution Computation, Germany, 1997.

  14. Lee, M. A. and Takagi, H.: Integrating design stages on fuzzy systems using genetic algorithms, in: Second Internat. Conf. on Fuzzy Systems, 1993, pp. 612-617.

  15. Matellán, V., Fernández, C., and Molina, J. M.: Genetic learning of fuzzy reactive controllers, Robotics Autonom. Systems 25(1/2) (1998), 33-41.

    Google Scholar 

  16. Matellán, V., Molina, J. M., Sanz, J., and Fernández, C.: Learning fuzzy reactive behaviors in autonomous robots, in: Proc. of the 4th European Workshop on Learning Robots, Germany, 1995.

  17. McKerrow, P. J.: Introduction to Robotics, Addison-Wesley, Reading, MA, 1991.

    Google Scholar 

  18. Mitchell, M.: An Introduction to Genetic Algorithms, MIT Press, Cambridge, MA, 1996.

    Google Scholar 

  19. Molina, J. M., Sanchis, A., Berlanga, A., and Isasi-Viñuela, P.: Evolving connection weight between sensors and actuators in robots, in: IEEE Internat. Symposium on Industrial Electronics, 1997.

  20. Mondada, F.M. and Franzi, P.: Mobile robot miniaturization: A tool for investigation in control algorithms, in: Proc. of the 2nd Internat. Conf. on Fuzzy Systems, San Francisco, USA, 1993.

  21. Sanchis, A., Molina, J. M., and Isasi, P.: Classifier systems for learning reactions in robotic systems, in: The 1st Internat. Workshop on Machine Learning, Forecasting and Optimization (MALFO'96), 1996, pp. 153-159.

  22. Sanchis, A., Molina, J. M., and Isasi, P.: Learning reactive behavior for autonomous robots using classifier systems, in: F. L. Silva, J. C. Principe, and L. B. Almeida (eds), Spatiotemporal Models in Biological and Artificial Systems. Frontiers in Artificial Intelligence and Applications, Vol. 37, IOS Press, 1997, pp. 152-159.

  23. Shu, L. and Schaeffer, J.: HCS: Adding Hierarchies to classifier systems, in: 4th Int. Conf. on Genetic Algorithms, 1991, pp. 339-345.

  24. Sommaruga, L., Merino, I., Matellán, V., and Molina, J.: A distributed simulator for intelligent autonomous robots, in: Fourth Internat. Symp. on Intelligent Robotic Systems-SIRS'96, Lisboa, Portugal, 1996.

  25. Weiß, G.: Hierarchical chunking in classifier systems, in: Proc. of the 12th Internat. Conf. on Artificial Intelligence, 1994, pp. 1335-1340.

  26. Wilson, S.: Knowledge growth in an artificial animal, in: Proc. of the 1st Internat. Conf. on Genetic Algorithms and Their Applications, 1985, pp. 16-23.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sanchis, A., Molina, J.M., Isasi, P. et al. RTCS: a Reactive with Tags Classifier System. Journal of Intelligent and Robotic Systems 27, 379–405 (2000). https://doi.org/10.1023/A:1008195728465

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008195728465

Navigation