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Effect of Humans on Belief Propagation in Large Heterogeneous Teams

  • Praveen Paruchuri
  • Robin Glinton
  • Katia Sycara
  • Paul Scerri
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 40)

Summary

Members of large, heterogeneous teams often need to interact with different kinds of teammates to accomplish their tasks, teammates with dramatically different capabilities to their own. While the role of humans in teams has progressively decreased with the deployment of increasingly intelligent systems, they still have a major role to play. In this chapter, we focus on the role of humans in large, heterogeneous teams that are faced with situations, where there is a large volume of incoming, conflicting data about some important fact. We use an abstract model of both humans and agents to investigate the dynamics and emergent behaviors of large teams trying to decide whether some fact is true. In particular, we focus on the role of humans in handling noisy information and their role in convergence of beliefs in large heterogeneous teams. Our simulation results show that systems involving humans exhibit an enabler-impeder effect, where if humans are present in low percentages, they aid in propagating information; however when the percentage of humans increase beyond a certain threshold, they seem to impede the information propagation.

Keywords

Ground Truth Belief Propagation Self Organize Criticality Impeder Effect Avalanche Size 
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.

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References

  1. 1.
    Bak, P., Tang, C., Wiesenfeld, K.: Self-organized criticality. Phys. Rev. A 38(1), 364–374 (1988) CrossRefMathSciNetGoogle Scholar
  2. 2.
    Banerjee, A.: A simple model of herd behavior. Q. J. Econ. 107(3), 797–817 (1992) CrossRefGoogle Scholar
  3. 3.
    Bellur, B., Lewis, M., Templin, F.: An ad-hoc network for teams of autonomous vehicles. In: Proc. of First Annual Symposium on Autonomous Intelligence Networks and Systems (2002) Google Scholar
  4. 4.
    Bikhchandani, S., Hirshleifer, D., Welch, I.: A theory of fads, fashion, custom, and cultural change as informational cascades. J. Polit. Econ. 100(5), 992–1026 (1992) CrossRefGoogle Scholar
  5. 5.
    Clar, S., Drossel, B., Schwabl, F.: Scaling laws and simulation results for the self-organized critical forest-fire model. Phys. Rev. E 50(2), 1009–1018 (1994) CrossRefGoogle Scholar
  6. 6.
    Dekker, A.H.: Centralisation and decentralisation in network centric warfare. J. Battlef. Technol. 6, 23–28 (2003) Google Scholar
  7. 7.
    Glinton, R., Paruchuri, P., Scerri, P., Sycara, K.: Self organized criticality of belief propagation in large heterogeneous teams. In: Hirsch, M.J., Pardalos, P., Murphey, R. (eds.) Dynamics of Information Systems: Theory and Applications. Springer Optimization and its Applications. Springer, Berlin (2010) Google Scholar
  8. 8.
    Malthe-Sørenssen, A., Feder, J., Christensen, K., Frette, V., Jøssang, T.: Surface fluctuations and correlations in a pile of rice. Phys. Rev. Lett. 83(4), 764–767 (1999) CrossRefGoogle Scholar
  9. 9.
    Motter, A., Lai, Y.: Cascade-based attacks on complex networks. Phys. Rev. E 66(6), 065102 (2002) CrossRefGoogle Scholar
  10. 10.
    Neill, D.: Cascade effects in heterogeneous populations. Ration. Soc. 17(2), 191–241 (2005) CrossRefGoogle Scholar
  11. 11.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003) MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Olami, Z., Feder, H.J.S., Christensen, K.: Self-organized criticality in a continuous, nonconservative cellular automaton modeling earthquakes. Phys. Rev. Lett. 68(8), 1244–1247 (1992) CrossRefGoogle Scholar
  13. 13.
    Sukthankar, G., Sycara, K., Giampapa, J.A., Burnett, M.C.: A model of human teamwork for agent-assisted search operations. In: Proceedings of the NATO Human Factors & Medicine Panel Symposium on Adaptability in Coalition Teamwork. NATO RTA/HFM (2008) Google Scholar
  14. 14.
    Watts, D.: A simple model of global cascades on random networks. Proc. Nat. Acad. Sci. USA 99(9), 5766–5771 (2002) MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Praveen Paruchuri
    • 1
  • Robin Glinton
    • 1
  • Katia Sycara
    • 1
  • Paul Scerri
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA

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