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Dagstuhl Seminar 11172

Artificial Immune Systems

( Apr 25 – Apr 29, 2011 )


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Please use the following short url to reference this page: https://www.dagstuhl.de/11172

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Summary

Artificial immune systems (AISs) are inspired by biological immune systems and mimic these by means of computer simulations. They are seen with interest from immunologists as well as engineers. Immunologists hope to gain a deeper understanding of the mechanisms at work in biological immune systems. Engineers hope that these nature-inspired systems prove useful in very difficult computational tasks, ranging from applications in intrusion-detection systems to general optimization. Moreover, computer scientists identified artificial immune systems as another example of a nature-inspired randomized search heuristic (like evolutionary algorithms, ant colony optimization, particle swarm optimization, simulated annealing, and others) and aim at understanding their potential and limitations. While the relatively new field has its successful applications and much potential its theoretical foundation is still in its infancy. Currently there are several not well connected strands within AIS theory, not even a general agreement on what the central open problems are, and only a weak connection between AIS theory and AIS applications. The main goals of the proposed seminar include bringing together computer scientists and engineers to strengthen the connections within AIS theory, connections to other researchers working on the theory of randomized search heuristics, and to improve connectivity between AIS theory and applications.

Biological immune systems show great resilience in harsh environments and demonstrate the ability to cope with large amounts of sensory data as well as the unpredictability of the natural world. Indeed, a great deal of attention is now being paid to these aspects of the immune system by the wider computing research community.

Given the practical success of AIS, there is a serious lack of theoretical work in the area. Many AIS algorithms are based purely on clonal selection mechanisms, without any interaction between the different members of the cell populations. The dynamics of cell populations in the immune system have been modeled extensively using nonlinear dynamical systems. At present, however, there is no centrally agreed approach on how to tackle important theoretical issues in AIS. All too often theory is undertaken without the due attention to the practical implications. For theory to have a serious impact, collaboration between theoreticians and engineers is needed to identify key engineering issues, relevant theoretical issues and crucially how the theory can help support the engineering process. While starting point of the seminar and its driving force are deficits in the theoretical foundation of AIS its main goals are clearly beyond theory. At the heart of the seminar's motivation is the conviction that there is nothing more practical than a good theory.

The seminar took place from April 26th to April 29th 2011. It started with a series of talks aimed at providing a suitable level of introduction to the main areas of discussion to provide a levelling ground for all participants. The format of the seminar was then a series of short presentations by researchers on topics that ranged from swarm robotics to immunology and theoretical frameworks for algorithm analysis. These were then followed by a series of {\em breakout} group sessions which focussed discussion on the issues raised by the speakers with results from those discussions being reported back to the main group at regular intervals. Towards the end of the week, a convergence into four key topics emerges: (1) The principled development of bio-inspired algorithms and how the translation from computational models into usable algorithms is managed, (2) the relationship between evolution and immunity and how it might be possible to evolve an artificial immune system in complex engineering problems, specifically swarm robotic systems, (3) the development of a definitive clonal selection algorithm with appropriate theoretical analysis and (4) the development of novel immune algorithms and the use of models from computational immunology for both the understanding of immunological processes and the development of new algorithms. These four topics are to be taken forward as journal papers by participants from the seminar.

As a result of the seminar there will be a special issue published in Natural Computing a leading journal in the area that will not only publish papers outlined above, but provide a roadmap for the future direction of AIS and serve as, it is hoped, an authoritative guide to the area of artificial immune systems.


Participants
  • Uwe Aickelin (University of Nottingham, GB)
  • Luca Albergante (University of Milan, IT)
  • Bruno Apoloni (University of Milan, IT)
  • Helio J.C. Barbosa (Lab. Nacional de Computação Científica-Petrópolis, BR)
  • Ed Clark (University of York, GB)
  • George M. Coghill (University of Aberdeen, GB)
  • Benjamin Doerr (MPI für Informatik - Saarbrücken, DE) [dblp]
  • Julie Greensmith (University of Nottingham, GB)
  • Emma Hart (Edinburgh Napier University, GB) [dblp]
  • Christian Jacob (University of Calgary, CA) [dblp]
  • Thomas Jansen (University College Cork, IE) [dblp]
  • Per Kristian Lehre (University of Nottingham, GB) [dblp]
  • Chris McEwan (Edinburgh Napier University, GB)
  • Yevgen Nebesov (Leibniz Universität Hannover, DE)
  • Robert Oates (University of Nottingham, GB)
  • Pietro S. Oliveto (University of Birmingham, GB) [dblp]
  • Mathias Pacher (Goethe-Universität - Frankfurt a. M., DE)
  • Mark Read (University of York, GB)
  • Thomas Stibor (TU München, DE)
  • Dirk Sudholt (University of Sheffield, GB) [dblp]
  • Johannes Textor (Utrecht University, NL)
  • Jon Timmis (University of York, GB) [dblp]
  • Alan FT Winfield (University of the West of England - Bristol, GB) [dblp]
  • Carsten Witt (Technical University of Denmark - Lyngby, DK) [dblp]
  • Lidia Yamamoto (Université de Strasbourg - Strasbourg, FR)
  • Christine Zarges (University of Birmingham, GB) [dblp]

Classification
  • artificial immune systems
  • randomized search heuristics
  • artificial intelligence
  • algorithms

Keywords
  • artificial immune systems
  • bio-inspired search heuristics
  • modeling
  • theoretical analysis