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Distributed Agent Evolution with Dynamic Adaptation to Local Unexpected Scenarios

  • Suranga Hettiarachchi
  • William M. Spears
  • Derek Green
  • Wesley Kerr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3825)

Abstract

This paper introduces a novel framework for designing multi-agent systems, called “Distributed Agent Evolution with Dynamic Adaptation to Local Unexpected Scenarios” (DAEDALUS). Traditional approaches to designing multi-agent systems are offline (in simulation), and assume the presence of a global observer. In the online (real world), there may be no global observer, performance feedback may be delayed or perturbed by noise, agents may only interact with their local neighbors, and only a subset of agents may experience any form of performance feedback. Under these circumstances, it is much more difficult to design multi-agent systems. DAEDALUS is designed to address these issues, by mimicking more closely the actual dynamics of populations of agents moving and interacting in a task environment. We use two case studies to illustrate the feasibility of this approach.

Keywords

Genetic Algorithm Obstacle Avoidance Performance Feedback Dynamic Adaptation Credit Assignment 
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 2006

Authors and Affiliations

  • Suranga Hettiarachchi
    • 1
  • William M. Spears
    • 1
  • Derek Green
    • 1
  • Wesley Kerr
    • 1
  1. 1.University of WyomingLaramieUSA

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