Skip to main content
SpringerLink
Log in

Two Paradigms for the Design of Artificial Collectives

  • Chapter
Collectives and the Design of Complex Systems

Summary

Artificial collectives are systems composed of multiple autonomous information or software agents, mobile robots, or nodes in a sensor or communication network. In the future, such systems will be responsible for many important tasks, such as highway traffic control, disaster response, toxic spill monitoring and cleanup, and exploration of other planets. Because such systems will have to function in environments with unreliable communication channels, where agents are likely to fail, they will have to be reliable, scalable, robust, adaptable, and amenable to quantitative mathematical analysis. The last property is important because analysis is crucial to understanding the issues of the design, control, adaptability, and dynamics of collective behavior. We describe two approaches to distributed control of artificial collectives and study them quantitatively. The first, biologically based control, relies on local interactions among many simple agents to create desirable collective behavior. The second approach allows collectives to maximize their world utility using market-based mechanisms. We present two applications—foraging in a group of robots and resource allocation in dynamic environments—that use these control paradigms and perform an analysis of each problem.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
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. W. Agassounon, A. Martinoli, and R. M. Goodman. A scalable, distributed algorithm for allocating workers in embedded systems. In Proc. of the IEEE Conf. on System, Man and Cybernetics SMC-01, October 2001, Tucson, AR, USA, to appear. 2001.

    Google Scholar 

  2. R. C. Arkin. Behavior-Based Robotics. The MIT Press, Cambridge, MA, 1999.

    Google Scholar 

  3. B. W. Arthur. Inductive reasoning and bounded rationality. Am. Econ. Assoc. Papers Proc., 84:406, 1994.

    Google Scholar 

  4. A.-L. Barabasi and H. E. Stanley. Fractal Concepts in Surface Growth. Cambridge University Press, 1995.

    Google Scholar 

  5. R. Beckers, O. E. Holland, and J. L. Deneubourg. From local actions to global tasks: Stigmergy and collective robotics. In Rodney A. Brooks and Pattie Maes, editors, Proc. of the 4th International Workshop on the Synthesis and Simulation of Living Systems ArtificialLife IV, pages 181–9, Cambridge, MA, USA, July 1994. MIT Press.

    Google Scholar 

  6. G. Beni and J. Wang. Swarm intelligence. In Proc. of the Seventh Annual Meeting of the Robotics Society of Japan, Tokyo, Japan, pages 425–8, Tokyo, Japan, 1989. RSJ Press.

    Google Scholar 

  7. A. Billard, A.J. Ijspeert, and A. Martinoli. A multi-robot system for adaptive exploration of a fast changing environment: Probabilistic modelling and experimental study. Connection Science, 11(3/4):359–79, 1999.

    Article  Google Scholar 

  8. K. Boehringer, R. Brown, B. Donald, J. Jennings, and D. Rus. Distributed robotic manipulation: Experiments in minimalism. In O. Khatib and J. K. Salisbury, editors, Proc. of the Fourth Int. Symp. on Experimental Robotics, Stanford, pages 11–25. Lecture Notes in Control and Information Sciences, Springer-Verlag, 1995.

    Google Scholar 

  9. E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York, 1999.

    MATH  Google Scholar 

  10. A. Cavagna. Irrelevance of memory in the minority game. Phys. Rev., E59:R3783, 1998.

    Google Scholar 

  11. D. Challet and M. Marsili. Phase transition and symmetry breaking in the minority game. Phys. Rev., E60:R6271, 1999.

    Google Scholar 

  12. D. Challet and Y.-C. Zhang. Emergence of cooperation and organization in an evolutionary game. Physica, A246:407, 1997.

    Google Scholar 

  13. D. Challet and Y.-C. Zhang. On the minority game: Analytical and numerical studies. Physica, A256:514, 1998.

    Google Scholar 

  14. D. Chowdhury, L. Santen, and A. Schadschneider. Statistical physics of vehicular traffic and some related systems. Physics Reports, 329:199, 2000.

    Article  MathSciNet  Google Scholar 

  15. M. A. R. de Cara, O. Pla, and F. Guinea. Learning, competition and cooperation in simple games. Eur. Phys. J., B13:413, 2000.

    Google Scholar 

  16. B. Derrida and Y. Pomeau. Random networks of automata: A simple annealed approximation. Eur. Phys. Lett., 1:45, 1986.

    Article  Google Scholar 

  17. C. W. Gamier. Handbook of Stochastic Methods. Springer, New York, NY, 1983.

    Google Scholar 

  18. D. Goldberg and Maja J Mataric. Robust behavior-based control for distributed multirobot collection tasks. Technical Report IRIS-00-387, USC Institute for Robotics and Intelligent Systems, 2000.

    Google Scholar 

  19. R. Haberman. Mathematical Models: Mechanical Vibrations, Population Dynamics, and Traffic Flow. Society of Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 1998.

    Book  MATH  Google Scholar 

  20. A. T. Hayes, A. Martinoli, and R. M. Goodman. Comparing distributed exploration strategies with simulated and real autonomous robots. In L. E. Parker, G. Bekey, and J. Bahren, editors, Proc. of the Fifth Int. Symp. on Distributed Autonomous Robotic Systems DARS-00, October, 2000, Knoxville, TN, pp. 261–70. Springer-Verlag, 2000.

    Google Scholar 

  21. A. T. Hayes, A. Martinoli, and R. M. Goodman. Swarm robotic odor localization. In Proc. of the IEEE Conf. on Intelligent Robots and Systems IROS-01, October-November 2001, Maui, HI, USA. 2001.

    Google Scholar 

  22. D. Helbing. Quantitative Sociodynamics: Stochastic Methods and Models of Social Interaction Processes, volume 31 of Theory and Decision Library B: Mathematical and Statistical Methods. Kluwer Academic, Dordrecht, 1995.

    Google Scholar 

  23. O. Holland and C. Melhuish. Stigmergy, self-organization, and sorting in collective robotics. Artificial Life, 5:173–202, 1999.

    Article  Google Scholar 

  24. B. A. Huberman and T. Hogg. The behavior of computational ecologies. In B. A. Huberman, editor, The Ecology of Computation, pages 77–115, Amsterdam, 1988. Elsevier (North-Holland).

    Google Scholar 

  25. A. J. Ijspeert, A. Martinoli, A. Billard, and L. M. Gambardella. Collaboration through the exploitation of local interactions in autonomous collective robotics: The stick pulling experiment. Autonomous Robots, 11(2):149–71, 2001.

    Article  MATH  Google Scholar 

  26. N. F. Johnson, P. M. Hui, D. Zheng, and C. W. Tai. Minority game with arbitrary cuttoffs. Physica, A269:493, 1999.

    Google Scholar 

  27. P. J. Johnson and J. S. Bay. Distributed control of simulated autonomous mobile robot collectives in payload transportation. Autonomous Robots, 2:43–63, 1995.

    Article  Google Scholar 

  28. T. Kalinowski, H.-J. Schulz, and M. Briese. Cooperation in the minority game with local information. Physica, A277:502, 2000.

    Google Scholar 

  29. N. G. Van Kampen. Stochastic Processes in Physics and Chemistry. Elsevier Science, Amsterdam, revised and enlarged edition, 1992.

    Google Scholar 

  30. S. A. Kauffman. The Origins of Order. Oxford University Press, New York, 1993.

    Google Scholar 

  31. J. O. Kephart, T. Hogg, and B. A. Huberman. Collective behavior of predictive agents. Physica, D 42:48–65, 1990.

    Google Scholar 

  32. M. J. B. Krieger and J.-B. Billeter. The call of duty: Self-organised task allocation in a population of up to twelve mobile robots. Robotics and Autonomous Systems, 30(1–2): 65–84, 2000.

    Article  Google Scholar 

  33. C. R. Kube and E. Bonabeau. Cooperative transport by ants and robots. Robotics and Autonomous Systems, 30(1-2):85–101, 2000.

    Article  Google Scholar 

  34. K. Lerman and A. Galstyan. Mathematical model of foraging in a group of robots: Effect of interference. Autonomous Robots, 13(2), 2002.

    Google Scholar 

  35. K. Lerman, A. Galstyan, A. Martinoli, and A. Ijspeert. A macroscopic analytical model of collaboration in distributed robotic systems. Artificial Life Journal, 7(4):375–93, 2001.

    Article  Google Scholar 

  36. A. Martinoli. Swarm Intelligence in Autonomous Collective Robotics: From Tools to the Analysis and Synthesis of Distributed Control Strategies. Ph.D. thesis, No. 2069, EPFL, 1999.

    Google Scholar 

  37. A. Martinoli. Swarm Intelligence in Autonomous Collective Robotics: From Tools to the Analysis and Synthesis of Distributed Control Strategies. Ph.D. thesis, No. 2069, EPFL, 1999.

    Google Scholar 

  38. A. Martinoli, A. J. Ijspeert, and L. M. Gambardella. A probabilistic model for understanding and comparing collective aggregation mechanisms. In Dario Floreano Jean-Daniel Nicoud, and Francesco Mondada, editors, Proc. of the 5th European Conference on Advances in Artificial Life (ECAL-99), volume 1674 of LNAI, pp. 575–84, Berlin, September 13-17 1999. Springer.

    Google Scholar 

  39. A. Martinoli, A.J. Ijspeert, and F. Mondada. Understanding collective aggregation mechanisms: From probabilistic modelling to experiments with real robots. Robotics and Autonomous Systems, 29:51–63, 1999.

    Article  Google Scholar 

  40. A. Martinoli and F. Mondada. Collective and cooperative group behaviors: Biologically inspired experiments in robotics. In O. Khatib and J. K. Salisbur, editors, Proc. of the Fourth Int. Symp. on Experimental Robotics ISER-95. Springer Verlag, June-July 1995.

    Google Scholar 

  41. M. J. Matarić, M. Nilsson, and K. Simsarian. Cooperative multi-robot box pushing. In Proc. of the 1995 IEEE/RSJ International Conference on Intelligent Robots, 1995.

    Google Scholar 

  42. Maja Mataric. Interaction and Intelligent Behavior. Ph.D. thesis, Dept. of Electrical Engineering and Computer Science, MIT, Cambridge, MA, 1994.

    Google Scholar 

  43. O. Michel. Webots: Symbiosis between virtual and real mobile robots. In J.-C. Heudin, editor, Proc. of the First Int. Conf. on Virtual Worlds, Paris, France, pp. 254–63. Springer-Verlag, 1998. See also http://www.cyberbotics.com/webots/

  44. See http://www.unifr.ch/econophysics/minority/ for an extensive collection of articles and references.

  45. J. R. Norris. Markov Chains. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, Cambridge, UK, 1997.

    Google Scholar 

  46. S. W. Pacala, D. M. Gordon, and H. C. J. Godfray. Effects of social group size on information transfer and task allocation. Evolutionary Ecology, 10:127–65, 1996.

    Article  Google Scholar 

  47. M. Paczuski and K. E. Bassler. Self-organized networks of competing Boolean agents. Phys. Rev. Lett, 84:3185, 2000.

    Article  Google Scholar 

  48. J. K. Parrish and W. M. Hamner. Animal Groups in Three Dimensions. Cambridge University Press, 1997.

    Google Scholar 

  49. R. Savit, R. Manuca, and R. Riolo. Adaptive competition, market efficiency, phase transition. Phys. Rev. Lett, 82(10):2203, 1999.

    Article  Google Scholar 

  50. F. Schweitzer, K. Lao, and F. Family. Active random walkers simulate trunk trail formation by ants. BioSystems, 41:153–66, 1997.

    Article  Google Scholar 

  51. C. Shalizi. private communication.

    Google Scholar 

  52. K. Sugawara and M. Sano. Cooperative acceleration of task performance: Foraging behavior of interacting multi-robots system. Physica, D100:343–54, 1997.

    Google Scholar 

  53. K. Sugawara, M. Sano, and I. Yoshihara. Cooperative acceleration of task performance: Analysis of foraging behavior by interacting multi-robots. In Proc. IPSJ Int. Symp. on Information Systems and Technologies for Network Society, pp. 314–17, Fukuoka, Japan, September 1997.

    Google Scholar 

  54. K. Sugawara, M. Sano, I. Yoshihara, and K. Abe. Cooperative behavior of interacting robots. Artificial Life and Robotics, 2:62–7, 1998.

    Article  Google Scholar 

  55. R. T. Vaughan, K. Støy, G. S. Sukhatme, and M. J. Mataric. Blazing a trail: Insect-inspired resource transportation by a robot team. In Proc. of the 5th International Symposium on Distributed Autonomous Robotic Systems (DARS), Knoxville, TN, 2000.

    Google Scholar 

  56. M. P. Wellman. Market-oriented programming: Some early lessons. In S. H. Clearwater, editor, Market-Based Control: A Paradigm for Distributed Resource Allocation, pp. 74–95. World Scientific, January 1996.

    Google Scholar 

  57. S. Wolfram. Cellular Automata and Complexity. Addison-Wesley, Reading, MA, 1994.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA

    Kristina Lerman & Aram Galstyan

Authors
  1. Kristina Lerman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aram Galstyan
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computational Sciences Division, NASA Ames Research Center, Moffett Field, CA, 94035, USA

    Kagan Tumer  & David Wolpert  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer Science+Business Media New York

About this chapter

Cite this chapter

Lerman, K., Galstyan, A. (2004). Two Paradigms for the Design of Artificial Collectives. In: Tumer, K., Wolpert, D. (eds) Collectives and the Design of Complex Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8909-3_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-4419-8909-3_10

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-6472-9

  • Online ISBN: 978-1-4419-8909-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation