Concurrent Modeling of Alternative Worlds with Polyagents

  • H. Van Dyke Parunak
  • Sven Brueckner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4442)

Abstract

Agent-based modeling is a powerful tool for systems modeling. Instantiating each domain entity with an agent captures many aspects of system dynamics and interactions that other modeling techniques do not. However, an entity’s agent can execute only one trajectory per run, and does not sample the alternative trajectories accessible to the entity in the evolution of a realistic system. Averaging over multiple runs does not capture the range of individual interactions involved. We address these problems with a new modeling entity, the polyagent, which represents each entity with a single persistent avatar supported by a swarm of transient ghosts. Each ghost interacts with the ghosts of other avatars through digital pheromone fields, capturing a wide range of alternative trajectories in a single run that can proceed faster than real time.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brueckner, S.: Return from the Ant: Synthetic Ecosystems for Manufacturing Control. Dr.rer.nat. Thesis at Humboldt University Berlin, Department of Computer Science (2000), http://dochost.rz.hu-berlin.de/dissertationen/brueckner-sven-2000-06-21/PDF/Brueckner.pdf
  2. 2.
    Dorigo, M., Stuetzle, T.: Ant Colony Optimization. MIT Press, Cambridge, MA (2004)MATHGoogle Scholar
  3. 3.
    Feynman, R., Hibbs, A.R.: Quantum Mechanics and Path Integrals. McGraw-Hill, New York (1965)MATHGoogle Scholar
  4. 4.
    Jacob, C.: Illustrating Evolutionary Computation With Mathematica. Morgan Kaufmann, San Francisco (2001)Google Scholar
  5. 5.
    Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge, UK (1997)MATHGoogle Scholar
  6. 6.
    Kijima, K.: Why Stratification of Networks Emerges in Innovative Society: Intelligent Poly-Agent Systems Approach. Computational and Mathematical Organization Theory 7(1), 45–62 (2001)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Kott, A.: Real-Time Adversarial Intelligence & Decision Making (RAID) (2004), http://dtsn.darpa.mil/ixo/programdetail.asp?progid=57
  8. 8.
    Lambert, T.J., Epelman III, M.A., Smith, R.L.: A Fictitious Play Approach to Large-Scale Optimization. Operations Research 53(3) (2005)Google Scholar
  9. 9.
    Parunak, H.V.D.: Go to the Ant: Engineering Principles from Natural Agent Systems. Annals of Operations Research 75, 69–101 (1997), http://www.newvectors.net/staff/parunakv/gotoant.pdf MATHCrossRefGoogle Scholar
  10. 10.
    Parunak, H.V.D.: Making Swarming Happen. In: Proceedings of Swarming and Network-Enabled C4ISR, Tysons Corner, VA, ASD C3I (2003), http://www.newvectors.net/staff/parunakv/MSH03.pdf
  11. 11.
    Parunak, H.V.D., Belding, T.C., Brueckner, S.: Prediction Horizons in Polyagent Models. In: Proceedings of Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2007), Honolulu, HI, pp. 930–932 (2007), http://www.newvectors.net/staff/parunakv/AAMAS07PH.pdf
  12. 12.
    Parunak, H.V.D., Brueckner, S.: Entropy and Self-Organization in Multi-Agent Systems. In: Proceedings of The Fifth International Conference on Autonomous Agents (Agents 2001), Montreal, Canada, pp. 124–130. ACM Press, New York (2001), http://www.newvectors.net/staff/parunakv/agents01ent.pdf CrossRefGoogle Scholar
  13. 13.
    Parunak, H.V.D., Brueckner, S., Sauter, J.: Digital Pheromones for Coordination of Unmanned Vehicles. In: Weyns, D., Parunak, H.V.D., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 246–263. Springer, Heidelberg (2005), http://www.newvectors.net/staff/parunakv/E4MAS04_UAVCoordination.pdf Google Scholar
  14. 14.
    Parunak, H.V.D., Brueckner, S., Savit, R.: Universality in Multi-Agent Systems. In: Kudenko, D., Kazakov, D., Alonso, E. (eds.) Adaptive Agents and Multi-Agent Systems II. LNCS (LNAI), vol. 3394, pp. 930–937. Springer, Heidelberg (2005), http://www.newvectors.net/staff/parunakv/AAMAS04Universality.pdf Google Scholar
  15. 15.
    Parunak, H.V.D., Brueckner, S.A.: Extrapolation of the Opponent’s Past Behaviors. In: Kott, A., McEneany, W. (eds.) Adversarial Reasoning: Computational Approaches to Reading the Opponent’s Mind, pp. 49–76. Chapman and Hall/CRC Press, Boca Raton, FL (2006)Google Scholar
  16. 16.
    Parunak, H.V.D., Brueckner, S.A., Sauter, J.A., Matthews, R.: Global Convergence of Local Agent Behaviors. In: Proceedings of Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2005), Utrecht, The Netherlands, pp. 305–312. ACM Press, New York (2005), http://www.newvectors.net/staff/parunakv/AAMAS05Converge.pdf CrossRefGoogle Scholar
  17. 17.
    Parunak, H.V.D., Savit, R., Riolo, R.L.: Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users’ Guide. In: Sichman, J.S., Conte, R., Gilbert, N. (eds.) Multi-Agent Systems and Agent-Based Simulation. LNCS (LNAI), vol. 1534, pp. 10–25. Springer, Heidelberg (1998), http://www.newvectors.net/staff/parunakv/mabs98.pdf Google Scholar
  18. 18.
    Sauter, J.A., Matthews, R., Parunak, H.V.D., Brueckner, S.: Evolving Adaptive Pheromone Path Planning Mechanisms. In: Alonso, E., Kudenko, D., Kazakov, D. (eds.) Adaptive Agents and Multi-Agent Systems. LNCS (LNAI), vol. 2636, pp. 434–440. Springer, Heidelberg (2003), http://www.newvectors.net/staff/parunakv/AAMAS02Evolution.pdf Google Scholar
  19. 19.
    Sauter, J.A., Matthews, R., Parunak, H.V.D., Brueckner, S.A.: Performance of Digital Pheromones for Swarming Vehicle Control. In: Proceedings of Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems, Utrecht, Netherlands, pp. 903–910. ACM Press, New York (2005), http://www.newvectors.net/staff/parunakv/AAMAS05SwarmingDemo.pdf CrossRefGoogle Scholar
  20. 20.
    Savit, R., Brueckner, S.A., Parunak, H.V.D., Sauter, J.: Phase Structure of Resource Allocation Games. Physics Letters A 311, 359–364 (2002), http://arxiv.org/pdf/nlin.AO/0302053 CrossRefGoogle Scholar
  21. 21.
    Shnerb, N.M., Louzoun, Y., Bettelheim, E., Solomon, S.: The importance of being discrete: Life always wins on the surface. Proc. Natl. Acad. Sci. USA 97(19), 10322–10324 (2000), http://www.pnas.org/cgi/reprint/97/19/10322 MATHCrossRefGoogle Scholar
  22. 22.
    Sterman, J.: Business Dynamics. McGraw-Hill, New York (2000)Google Scholar
  23. 23.
    Wittig, T.: ARCHON: An Architecture for Multi-agent Systems. Ellis Horwood, New York (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • H. Van Dyke Parunak
    • 1
  • Sven Brueckner
    • 1
  1. 1.NewVectors LLC, 3520 Green Court, Suite 250, Ann Arbor, MI 48105USA

Personalised recommendations