Journal of Intelligent and Robotic Systems

, Volume 32, Issue 1, pp 93–114 | Cite as

ABC2 an Agenda Based Multi-Agent Model for Robots Control and Cooperation

  • Vicente Matellán
  • Daniel Borrajo
Article

Abstract

This paper presents a model for the control of autonomous robots that allows cooperation among them. The control structure is based on a general purpose multi-agent architecture using a hybrid approach made up by two levels. One level is composed of reactive skills capable of achieving simple actions by their own. The other one uses an agenda used as an opportunistic planning mechanism to compound, activate and coordinate the basic skills. This agenda handles actions both from the internal goals of the robot or from other robots. This two level approach allows the integration of real-time response of reactive systems needed for robot low-level behavior, with a classical high level planning component that permits a goal oriented behavior. The paper describes the architecture itself, and its use in three different domains, including real robots, as well as the issues arising from its adaptation to the RoboCup simulator domain.

agenda control cooperation fuzzy multi-agent robots 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arkin, R. C.: Motor schema based mobile robot navigation, J. Robotics Res. 8(4) (1989), 92-112.Google Scholar
  2. Arkin, R. C. and Balch, T. R.: AuRA: principles and practice in review, J. Experimental and Theoretical Artificial Intelligence 9(2) (1997).Google Scholar
  3. Blum, A. L. and Furst, M. L.: Fast planning through planning graph analysis, in: C. S. Mellish (ed.), Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI-95, Vol. 2, Montreal (Canada), 1995, pp. 1636-1642.Google Scholar
  4. Bond, A. H. and Gasser, L.: Readings in Distributed Artificial Intelligence, Morgan Kaufmann, 1988.Google Scholar
  5. Brooks, R. A.: A roboust layered control system for a mobile robot, IEEE J. Robotics Automat. RA-2(1) (1986), 14-23.Google Scholar
  6. Brooks, R. A.: Intelligence without representation, Artificial Intelligence 47 (1991), 139-159.Google Scholar
  7. Cohen, P. R. and Perrault, C. R.: Elements of a plan-based theory of speech acts, Cognitive Sci. RA-2(3) (1986), 177-212.Google Scholar
  8. Connell, J. H.: Minimalist Mobile Robotics: A Colony-style Architecture for a Mobile Robot, Academic Press, Cambridge, MA, 1990.Google Scholar
  9. Currie, K. and Tate, A.: O-plan: The open planning architecture, Artificial Intelligence 52(1) (1991), 49-86.Google Scholar
  10. Fernández, F., Borrajo, D., and Matellán, V.: VQQL: A model to generalize in reinforcement learning, in: Proceedings of the European Conference on Planning, Durham (UK), 1999, pp. 385-387.Google Scholar
  11. Fikes, R. E. and Nilsson, N. J.: STRIPS: A new approach to the application of theorem proving to problem solving, Artificial Intelligence 2 (1971), 189-208.Google Scholar
  12. Firby, J. R.: Modularity issues in reactive planning, in: Proceedings of the Third International Conference on AI Planning Systems, Edinburgh (UK), 1996, pp. 78-85.Google Scholar
  13. García-Martínez, R. and Borrajo, D.: An integrated approach of learning, planning, and execution, J. Intelligent Robotic Systems 29(1) (2000), 47-78.Google Scholar
  14. Georgeff, M. P. and Lansky, A. L.: Reactive reasoning and planning, in: Proceedings of AAAI-87 Sixth National Conference on Artificial Intelligence, Seattle, WA (USA), 1987, pp. 677-680.Google Scholar
  15. Hayes-Roth, B.: Opportunistic control of action in intelligent agents, IEEE Trans. Systems, Man, Cybernet. 23(6) (1992), 1575-1586.Google Scholar
  16. Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., and Osawa, E.: Robocup: The robot world cup initiative, in: Proceedings of the IJCAI-95 Workshop on Entertainment and AI/Life, 1995.Google Scholar
  17. Koza, J. R.: Evolving emergent wall following robotic behavior using the genetic programming paradigm, in: F. Varela and P. Bourgine (eds), Toward a Practice of Autonomus Systems. Proceedings of the First European Conference on Artificial Life, Cambridge, MA, 1991, pp. 110-119.Google Scholar
  18. Maes, P. and Brooks, R.: Learning to coordinate behaviors, in: Proccedings of the Eighth National Conference on Artificial Intelligence, San Mateo, CA, 1990, pp. 796-802.Google Scholar
  19. Matellán, V.: ABC 2: An architecture for intelligent autonomous systems, PhD Thesis, Dept. Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, 1998.Google Scholar
  20. Matellán, V., Borrajo, D., and Fernández, C.: Using ABC2 in the RoboCup domain, in: H. Kitano (ed.), RoboCup-97: Robot Soccer World Cup I, Lecture Notes in Artificial Intelligence, 1998, pp. 475-483.Google Scholar
  21. Matellán, V., Molina, J. M., and Fernández, C.: Fusion of fuzzy behaviors for autonomous robots, in: Proceedings of the Third International Symposium on Intelligent Robotic Systems, Pisa, Italy, 1995, pp. 157-164.Google Scholar
  22. Matellán, V., Molina, J. M., and Fernández, C.: Learning fuzzy behaviors for autonomous robots, in: Fourth European Workshop on Learning Robots, Karlsruhe, Germany, 1995, pp. 45-50.Google Scholar
  23. Matellán, V., Molina, J. M., and Sommaruga, L.: Fuzzy cooperation of autonomous robots, in: Proceedings of the Fourth International Symposium on Intelligent Robotic Systems, Lisboa, Portugal, 1996.Google Scholar
  24. Mondada, F., Franzi, E., and Ienne, P.: Mobile robot miniaturisation: A tool for investigation in contr ol algorithms, in: Proceedings of the Third International Symposium on Experimental Robotics, Kyoto, Japan, 1993.Google Scholar
  25. Muslea, I.: SINERGY: A linear planner based on genetic programming, in: Sam Steel (ed.), Recent Advances in AI Planning. 4th European Conference on Planning, ECP'97, Toulouse, France, 1997, pp. 312-324.Google Scholar
  26. Nilsson, N. J.: Shakey the robot, Technical Report 323, SRI A.I. Center, 1984.Google Scholar
  27. Noda, I.: Soccer server: A simulator of RoboCup, in: Proceedings of AI Symposium'95, 1995.Google Scholar
  28. Penberthy, J. S. and Weld, D. S.: UCPOP: A sound, complete, partial order planner for ADL, in: Proceedings of KR-92, 1992, pp. 103-114.Google Scholar
  29. Simmons, R., Goodwin, R., Zita, K., Koening, S., and O'Sullivan, J.: A layered architecture for office delivery robots, in: L. W. Johnson (ed.), Proceedings of First International Conference on Autonomous Agents, Marina del Rey, California (USA), 1997, pp. 245-252.Google Scholar
  30. Smith, R. G.: The contract net protocol: High-level communication and control in a distributed problem solver, IEEE Trans. Comput. C-29(12) (1980), 1104-1113.Google Scholar
  31. Sommaruga, L. and Catenazzi, N.: From practice to theory in designing autonomous agents, in: First Australian Workshop on Distributed Artificial Intelligence, Lectures Notes in Artif. Intell. 1087, Springer-Verlag, 1996, pp. 130-143.Google Scholar
  32. Sommaruga, L.,Merino, I.,Matellán, V., andMolina, J.M.: A distributed simulator for intelligent autonomous robots, in: Proceedings of the Fourth International Symposium on Intelligent Robotic Systems, Lisbon (Portugal), 1996, pp. 393-399.Google Scholar
  33. Steels, L.: Cooperation between distributed agents through self-organization, in: Jean-Pierre Muller (ed.), Descentralized A.I., Elsevier Science, 1990.Google Scholar
  34. Veloso, M., Carbonell, J., Pérez, A., Borrajo, D., Fink, E., and Blythe, J.: Integrating planning and learning: The PRODIGY architecture, J. Experiment. Theoret. Artif. Intell. 7 (1995), 81-120.Google Scholar
  35. Zadeh, L. A.: Outline of a new approach to the analysis of complex systems and decision processes, IEEE Trans. Systems, Man, Cybernet. 1 (1973).Google Scholar
  36. Zimmermann, H.-J.: Fuzzy Sets. Theory and its Application, Kluwer Acad. Publ., Boston, MA, 1990.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Vicente Matellán
    • 1
  • Daniel Borrajo
    • 2
  1. 1.Departamento de Ciencias Experimentales e IngenieríaUniversidad Rey Juan Carlos de MadridMóstoles (Madrid)Spain
  2. 2.Departamento de InformáticaUniversidad Carlos III de MadridLeganés (Madrid)Spain

Personalised recommendations