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A Genetic and Social Computational Model for the Emergence of Skill-Based Agent Specialization

  • Denton Cockburn
  • Ziad Kobti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7310)

Abstract

There are several methods that lead to the emergence of specialization in agent societies. Two such methods are the Genetic Threshold Model (GTM) and the Social Inhibition Model (SIM). Based on the premises of these models, such as the availability of social networks, or the presence of genetic thresholds, it is difficult to compare results across these models. We present a model that can mimic both these models, while aiming to increase the effect of agent skill on task choice when agents possess different aptitudes for tasks. Using a metric that quantifies the quality of work performed, we are able to see meaningful increases in work quality, but with a side effect of reduced levels of specialization.

Keywords

Hybrid Model Skill Level Task Choice Work Quality Maximum Threshold 
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-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Denton Cockburn
    • 1
  • Ziad Kobti
    • 1
  1. 1.School of Computer ScienceUniversity of WindsorWindsorCanada

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