Advertisement

Abstract

Multi-agent systems offer a new stage in the evolution of combat simulation. Originally, warfighters simulated combat manually to explore alternatives and plan their campaigns. The first applications of computers to combat simulation used algorithms that aggregated the warriors on each side, such as differential equations or game theory, effectively modeling the entire battlespace with a single process. Entity-based models such as OOS and Combat XXI assign a single agent to each entity, following the standard MAS agenda. A new modeling construct, the polyagent, takes this trend one step further, and uses several agents to model each construct. This approach addresses several challenges that face the traditional MAS approach, including fitting, closure, dynamism, and singularity. This chapter surveys the history of combat modeling, gives two examples of polyagent systems (one for planning, the other for adversarial prediction), and discusses how this construct addresses the challenges.

Keywords

Path Planning Autonomous Agent Place Agent Digital Pheromone Synthetic Evolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    I. Balogh and G. Harless, “An Overview of the COMBAT XXI Simulation Model: A Model for the Analysis of Land and Amphibious Warfare,” in Proc. of 71st Military Operations Research Society Symposium, USMCB Quantico, VA, 2003. http://www.mors.org/publications/abstracts/71morss/wg31_abs.htm.Google Scholar
  2. [2]
    S. Bonder and L. W. Farrell, Development of Models for Defense Systems Planning. SRL 2147 TR70-2, Systems Research Laboratory, University of Michigan, Ann Arbor, MI, 1970.Google Scholar
  3. [3]
    C. Brosilow and B. Joseph, Techniques of Model-Based Control. Upper Saddle River, NJ, Prentice Hall PTR, 2002.Google Scholar
  4. [4]
    S. Brueckner, 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.Google Scholar
  5. [5]
    W. Collischonn and J. V. Pilar, “A direction dependent least-cost-path algorithm for roads and canals,” International Journal of Geographical Information Science, 14(4), pp. 397–406, 2000.CrossRefGoogle Scholar
  6. [6]
    D. H. Douglas, “Least-cost path in GIS using an accumulated cost surface and slope lines,” Cartographica, 31:37–51, 1994.Google Scholar
  7. [7]
    Eternal Egypt. Model of Soldiers of Mesehti, Egyptian Pikemen. 2005. http://www.eternalegypt.org/EternalEgyptWebsiteWeb/HomeServlet?ee_website_action_key=action.display.element&story_id=5&module_id=31&language_id=1&element_id=6 0577.Google Scholar
  8. [8]
    P.-P. Grass, “La Reconstruction du nid et les Coordinations Inter-Individuelles chez Bellicositermes Natalensis et Cubitermes sp. La thorie de la Stigmergie: Essai d’interprtation du Comportement des Termites Constructeurs,” Insectes Sociaux, 6:41–84, 1959.CrossRefGoogle Scholar
  9. [9]
    A. Ilachinski, Artificial War: Multiagent-based Simulation of Combat. Singapore, World Scientific, 2004.Google Scholar
  10. [10]
    S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by Simulated Annealing,” Science, 220:671–80, 1983.CrossRefMathSciNetGoogle Scholar
  11. [11]
    F. W. Lanchester, Aircraft in Warfare: The Dawn of the Fourth Arm. London, Constable and Co, Ltd., 1916.Google Scholar
  12. [12]
    M. K. Lauren and R. T. Stephen, “Map-Aware Non-uniform Automata (MANA)A New Zealand Approach to Scenario Modelling,” Journal of Battlefield Technology, 5(1 (March)):27ff, 2002. http://www.argospress.com/jbt/Volume5/5-1-4.htm.Google Scholar
  13. [13]
    H. V. D. Parunak, “Evolving Swarming Agents in Real Time,” in Proc. of Genetic Programming Theory and Practice (GPTP05), Ann Arbor, MI, Springer, 2005. http://www.newvectors.net/staff/parunakv/GPTP05.pdf.Google Scholar
  14. [14]
    H. V. D. Parunak, “Real-Time Agent Characterization and Prediction,” in Proc. of International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS’07), Industrial Track, Honolulu, Hawaii, pp. 1421–1428, ACM, 2007. http://www.newvectors.net/staff/parunakv/AAMAS07Fitting.pdf.Google Scholar
  15. [15]
    H. V. D. Parunak, R. Bisson, S. Brueckner, R. Matthews, and J. Sauter, “Representing Dispositions and Emotions in Simulated Combat,” in Proc. of Workshop on Defence Applications of Multi-Agent Systems (DAMAS05, at AAMAS05), Utrecht, Netherlands, pp. 51–65, Springer, 2005. http://www.newvectors.net/staff/parunakv/DAMAS05DETT.pdf.Google Scholar
  16. [16]
    H. V. D. Parunak and S. Brueckner, “Ant-LikeMissionaries and Cannibals: Synthetic Pheromones for Distributed Motion Control,” in Proc. of Fourth International Conference on Autonomous Agents (Agents 2000), Barcelona, ES, pp. 467–474, 2000. http://www.newvectors.net/staff/parunakv/MissCann.pdf.Google Scholar
  17. [17]
    H. V. D. Parunak and S. Brueckner, “Concurrent Modeling of Alternative Worlds with Polyagents,” in Proc. of the Seventh International Workshop on Multi-Agent-Based Simulation (MABS06, at AAMAS06), Hakodate, Japan, Springer, 2006. http://www.newvectors.net/staff/parunakv/MABS06Polyagents.pdf.Google Scholar
  18. [18]
    H. V. D. Parunak and S. Brueckner, “Modeling Uncertain Domains with Polyagents,” in Proc. of International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS’06), Hakodate, Japan, ACM, 2006. http://www.newvectors.net/staff/parunakv/AAMAS06Polyagents.pdf.Google Scholar
  19. [19]
    H. V. D. Parunak, S. Brueckner, M. Fleischer, and J. Odell, “A Design Taxonomy of Multi-Agent Interactions,” in Proc. of Agent-Oriented Software Engineering IV, Melbourne, AU, pp. 123–137, Springer, 2003. www.newvectors.net/staff/parunakv/cox.pdf.Google Scholar
  20. [20]
    H. V. D. Parunak, S. Brueckner, and J. Sauter, “Digital Pheromones for Coordination of Unmanned Vehicles,” in Proc. of Workshop on Environments for Multi-Agent Systems (E4MAS 2004), New York, NY, pages 246–263, Springer, 2004. http://www.newvectors.net/staff/parunakv/E4MAS04_UAVCoordination.pdf.Google Scholar
  21. [21]
    H. V. D. Parunak, S. Brueckner, and R. Savit, “Universality in Multi-Agent Systems,” in Proc. of Third International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2004), New York, NY, pp. 930–937, ACM, 2004. http://www.newvectors.net/staff/parunakv/AAMAS04Universality.pdf.Google Scholar
  22. [22]
    H. V. D. Parunak, S. A. Brueckner, and J. Sauter, “Digital Pheromone Mechanisms for Coordination of Unmanned Vehicles,” in Proc. of First International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2002), Bologna, Italy, pp. 449–450, ACM, 2002. www.newvectors.net/staff/parunakv/AAMAS02ADAPTIV.pdf.Google Scholar
  23. [23]
    H. V. D. Parunak, M. Purcell, and R. O’Connell, “Digital Pheromones for Autonomous Coordination of Swarming UAV’s,” in Proc. of First AIAA Unmanned Aerospace Vehicles, Systems, Technologies, and Operations Conference, Norfolk, VA, AIAA, 2002. www.newvectors.net/staff/parunakv/AIAA02.pdf.Google Scholar
  24. [24]
    E. Rimon and D. E. Kodischek, “Exact Robot Navigation Using Artificial Potential Functions,” IEEE Transactions on Robotics and Automation, 8(5 (October)), pp. 501–518, 1992.CrossRefGoogle Scholar
  25. [25]
    J. A. Sauter, R. Matthews, H. V. D. Parunak, and S. Brueckner, “Evolving Adaptive Pheromone Path Planning Mechanisms,” in Proc. of Autonomous Agents and Multi-Agent Systems (AAMAS02), Bologna, Italy, pages 434–440, ACM, 2002. www.newvectors.net/staff/parunakv/AAMAS02Evolution.pdf.Google Scholar
  26. [26]
    J. A. Sauter, R. Matthews, H. V. D. Parunak, and S. Brueckner, “Performance of Digital Pheromones for Swarming Vehicle Control,” in Proc. of Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems, Utrecht, Netherlands, pp. 903–910, ACM, 2005. http://www.newvectors.net/staff/parunakv/AAMAS05SwarmingDemo.pdf.Google Scholar
  27. [27]
    N. M. Shnerb, Y. Louzoun, E. Bettelheim, and S. Solomon, “The importance of being discrete: Life always wins on the surface,” in Proc. Natl. Acad. Sci. USA, 97(19 (September 12)), pp. 10322–10324, 2000. http://www.pnas.org/cgi/reprint/97/19/10322.MATHCrossRefGoogle Scholar
  28. [28]
    E. Stefanakis and M. Kavouras, “On the determination of the optimum path in space,” in Proc. of The European Conference on Spatial Information Theory (COSIT 95), Semmering, Austria, Springer, 1995.Google Scholar
  29. [29]
    B. Stilman, Linguistic Geometry: From Search to Construction. Boston, Kluwer, 2000.MATHGoogle Scholar
  30. [30]
    US Army PEO STRI. OneSAF Objective System (OOS). 2007. http://www.peostri.army.mil/PRODUCTS/ONESAF/.Google Scholar
  31. [31]
    J. von Neumann, “Zur Theorie der Gesellschaftsspiele‘,“ Mathematische Annalen, 100, pp. 295–320, 1928.CrossRefMathSciNetGoogle Scholar
  32. [32]
    J. von Neumann and O. Morgenstern, Theory of Games and Economic Behavior. Princeton, Princeton University Press, 1944.MATHGoogle Scholar
  33. [33]
    D. J. Watts and S. H. Strogatz, “Collective dynamics of “small-world” networks,” Nature, 393(6684 (4 June)), pp. 440–442, 1998.CrossRefGoogle Scholar
  34. [34]
    Wikipedia. Kriegspiel (wargame). 2007.Google Scholar
  35. [35]
    Wikipedia. Lotka-Volterra equation. 2007. http://en.wikipedia.org/wiki/Lotka-Volterra_equation.Google Scholar
  36. [36]
    W. G. Wilson, “Resolving Discrepancies between Deterministic Population Models and Individual-Based Simulations,” American Naturalist, 151(2), pp. 116–134, 1998.CrossRefGoogle Scholar

Copyright information

© Birkhäuser Verlag Basel/Switzerland 2007

Authors and Affiliations

  • H. Van Dyke Parunak
    • 1
  1. 1.NewVectorsAnn ArborUSA

Personalised recommendations