Effect of Humans on Belief Propagation in Large Heterogeneous Teams

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


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.


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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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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