ICARIS 2007: Artificial Immune Systems pp 204-215 | Cite as

The Application of a Dendritic Cell Algorithm to a Robotic Classifier

  • Robert Oates
  • Julie Greensmith
  • Uwe Aickelin
  • Jonathan Garibaldi
  • Graham Kendall
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4628)

Abstract

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

Keywords

False Negative Rate Danger Signal Anomaly Detection Safe Signal Dead Reckoning 
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 2007

Authors and Affiliations

  • Robert Oates
    • 1
  • Julie Greensmith
    • 1
  • Uwe Aickelin
    • 1
  • Jonathan Garibaldi
    • 1
  • Graham Kendall
    • 1
  1. 1.The University of Nottingham 

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