Skip to main content
Log in

Top-down vs bottom-up methodologies in multi-agent system design

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

Traditionally, two alternative design approaches have been available to engineers: top-down and bottom-up. In the top-down approach, the design process starts with specifying the global system state and assuming that each component has global knowledge of the system, as in a centralized approach. The solution is then decentralized by replacing global knowledge with communication. In the bottom-up approach, on the other hand, the design starts with specifying requirements and capabilities of individual components, and the global behavior is said to emerge out of interactions among constituent components and between components and the environment. In this paper we present a comparative study of both approaches with particular emphasis on applications to multi-agent system engineering and robotics. We outline the generic characteristics of both approaches from the MAS perspective, and identify three elements that we believe should serve as criteria for how and when to apply either of the approaches. We demonstrate our analysis on a specific example of load balancing problem in robotics. We also show that under certain assumptions on the communication and the external environment, both bottom-up and top-down methodologies produce very similar solutions.

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

  • Arkin, R., & Balch, T. (1998). Cooperative multiagent robotic systems. In D. Kortenkamp, R. P. Bonasso, & R. Murphy (Eds.), Artificial intelligence and mobile robots. Cambridge: MIT/AAAI Press.

    Google Scholar 

  • Bar-Shalom, Y., Li, X. R., & Kirubarajan, T. (2001). Estimation with application to tracking and navigation. New York: Wiley-Interscience.

    Google Scholar 

  • Bertsekas, D. P., & Tsitsiklis, J. N. (2000). Gradient convergence in gradient methods. SIAM Journal on Optimization, 10, 627–642.

    Article  MATH  MathSciNet  Google Scholar 

  • Chalupsky, H. et al. (2001). Electric elves: applying agent technology to support human organizations. In Proceedings of the thirteenth annual conference on innovative applications of artificial intelligence (IAAI-2001), Seattle, WA.

  • Crespi, V., & Cybenko, G. (2001). Agent-based systems engineering and intelligent vehicles and road systems. In: Darpa task program white paper. http://actcomm.thayer.dartmouth.edu/task/, April 2001.

  • Crespi, V., & Cybenko, G. (2003). Decentralized algorithms for sensor registration. In Proceedings of the 2003 international joint conference on neural networks (IJCNN2003), Portland, OR, July 2003.

  • Crespi, V., Cybenko, G., Rus, D., & Santini, M. (2002). Decentralized control for coordinated flow of multiagent systems. In Proceedings of the 2002 world congress on computational intelligence, Honolulu, HI, May 2002.

  • Cybenko, G. (2000). Agent-based systems engineering. In: Darpa task program research proposal. http://actcomm.thayer.dartmouth.edu/task/, October 2000.

  • Harper, C., & Winfield, A. F. T. (2006). A methodology for provably stable behavior-based intelligent control. Robotics and Autonomous Systems, 54, 52–73.

    Article  Google Scholar 

  • Holland, O., & Melhuish, C. (2000). Stigmergy, self-organization, and sorting in collective robotics. Artificial Life, 5, 173–202.

    Article  Google Scholar 

  • Ijspeert, A. J., Martinoli, A., Billard, A., & Gambardella, L. M. (2001). Collaboration through the exploitation of local interactions in autonomous collective robotics: the stick pulling experiment. Autonomous Robots, 11(2), 149–171.

    Article  MATH  Google Scholar 

  • Intanagonwiwat, C., Govindan, R., & Estrin, D. (2000). Directed diffusion: a scalable and robust communication paradigm for sensor networks. In ACM/IEEE international conference on mobile computing and networks (MobiCom 2000), Boston, MA, August 2000.

  • Isakowitz, T., Kamis, A., & Koufaris, M. (1998). Reconciling top-down and bottom-up design approaches in rmm. Data Base, 29(4), 58–67.

    Google Scholar 

  • Jones, C. V. (2005). A formal design methodology for coordinated multi-robot systems. PhD thesis, University of Southern California.

  • Jones, C. V., & Matarić, M. J. (2003a). From local to global behavior in intelligent self-assembly. In Proceedings of the IEEE international conference on robotics and automation (ICRA’03) (pp. 721–726), Taipei, Taiwan, September 2003.

  • Jones, C. V., & Matarić, M. J. (2003b). Adaptive task allocation in large-scale multi-robot systems. In Proceedings of the IEEE international conference on intelligent robots and systems (IROS’03) (pp. 1969–1974), Las Vegas, NV, October 2003.

  • Kornienko, S., Kornienko, O., & Levi, P. (2004). Generation of desired emergent behavior in swarm of micro-robots. In R. Lopez de Mantaras, & L. Saitta (Eds.), European conference on artificial intelligence (ECAI-04). Amsterdam: IOS Press.

    Google Scholar 

  • Kotz, D., Jiang, G., Gray, R., Cybenko, G., & Peterson, R. (2000) Performance analysis of mobile agents for filtering data streams on wireless networks (Technical report TR2000-366).

  • Kube, C., & Zhang, H. (1996) The use of perceptual cues in multi-robot box-pushing. In IEEE international conference on robotics and automation (pp. 2085–2090), Minneapolis, MN.

  • Lerman, K., & Galstyan, A. (2002). Mathematical model of foraging in a group of robots: effect of interference. Autonomous Robots, 13(2), 127–141.

    Article  MATH  Google Scholar 

  • Lerman, K., & Galstyan, A. (2003). Macroscopic analysis of adaptive task allocation in robots. In Proceedings of the IEEE international conference on intelligent robots and systems (IROS-2003) (pp. 1951–1956), Las Vegas, NV, October 2003.

  • Lerman, K., Galstyan, A., Martinoli, A., & Ijspeert, A. (2001). A macroscopic analytical model of collaboration in distributed robotic systems. Artificial Life Journal, 7(4), 375–393.

    Article  Google Scholar 

  • Lerman, K., Martinoli, A., & Galstyan, A. (2005). A review of probabilistic macroscopic models for swarm robotic systems. In E. Sahin & W. Spears (Eds.), Lecture notes in computer science : Vol. 3342. Swarm robotics workshop: state-of-the-art survey (pp. 143–152). Berlin: Springer.

    Google Scholar 

  • Lerman, K., Jones, C. V., Galstyan, A., & Matarić, M. J. (2006). Analysis of dynamic task allocation in multi-robot systems. International Journal of Robotics Research, 25(3), 225–242.

    Article  Google Scholar 

  • Martinoli, A., Ijspeert, A. J., & Gambardella, L. M. (1999). A probabilistic model for understanding and comparing collective aggregation mechanisms. In D. Floreano, J.-D. Nicoud, & F. Mondada (Eds.), Lecture notes in artificial intelligence : Vol. 1674. Proceedings of the 5th European conference on advances in artificial life (ECAL-99) (pp. 575–584). Berlin: Springer.

    Google Scholar 

  • Martinoli, A., Easton, K., & Agassounon, W. (2004). Modeling of swarm robotic systems: a case study in collaborative distributed manipulation. International Journal of Robotics Research, 23(4), 415–436.

    Article  Google Scholar 

  • McFarland, G. (1986). The benefits of bottom-up design. ACM SIGSOFT Software Engineering Notes, 11, 43–51.

    Article  Google Scholar 

  • McNew, J.-M., & Klavins, E. (2005). A grammatical approach to cooperative control. In CCOGraphGrammars.

  • Ott, M., & Lerman, K. (2007, submitted). Using grammar induction to synthesize robot controllers for dynamic task allocation. In IROS-07.

  • Pizka, M., & Bauer, A. (2004). A brief top-down and bottom-up philosophy on software evolution. In Principles of software evolution, 7th international workshop on (IWPSE’04), September 2004.

  • Sims, K. (1994). Evolving 3D morphology and behavior by competition. In R. Brooks & P. Maes (Eds.), Proceedings of artificial life IV (pp. 28–39).

  • Winfield, A. F. T., Sa, J., Fernandez-Gago, M. C., Dixon, C., & Fisher, M. (2005). On formal specification of emergent behaviors in swarm robotic systems. In Advanced robotic systems.

  • Wirth, N. (1971). Program development by stepwise refinement. Communications of the ACM, 14, 221–227.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Valentino Crespi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Crespi, V., Galstyan, A. & Lerman, K. Top-down vs bottom-up methodologies in multi-agent system design. Auton Robot 24, 303–313 (2008). https://doi.org/10.1007/s10514-007-9080-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-007-9080-5

Keywords

Navigation