Biologically-Inspired Concepts for Autonomic Self-protection in Multiagent Systems

  • Roy Sterritt
  • Mike Hinchey
Conference paper

DOI: 10.1007/978-3-642-04879-1_22

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4324)
Cite this paper as:
Sterritt R., Hinchey M. (2009) Biologically-Inspired Concepts for Autonomic Self-protection in Multiagent Systems. In: Barley M., Mouratidis H., Unruh A., Spears D., Scerri P., Massacci F. (eds) Safety and Security in Multiagent Systems. Lecture Notes in Computer Science, vol 4324. Springer, Berlin, Heidelberg


Biologically-inspired autonomous and autonomic systems (AAS) are essentially concerned with creating self-directed and self-managing systems based on metaphors from nature and the human body, such as the autonomic nervous system. Agent technologies have been identified as a key enabler for engineering autonomy and autonomicity in systems, both in terms of retrofitting into legacy systems and in designing new systems. Handing over responsibility to systems themselves raises concerns for humans with regard to safety and security. This paper reports on the continued investigation into a strand of research on how to engineer self-protection mechanisms into systems to assist in encouraging confidence regarding security when utilizing autonomy and autonomicity. This includes utilizing the apoptosis and quiescence metaphors to potentially provide a self-destruct or self-sleep signal between autonomic agents when needed, and an ALice signal to facilitate self-identification and self-certification between anonymous autonomous agents and systems.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Roy Sterritt
    • 1
  • Mike Hinchey
    • 2
  1. 1.School of Computing and MathematicsUniversity of UlsterNorthern Ireland
  2. 2.Lero-The Irish Software Engineering Research CentreUniversity of LimerickIreland

Personalised recommendations