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Of course, our current version does not even come close to the actual numbers
of interacting IS agents (e.g., billions of B cells within a small lymph node sec-
tion). However, according to our experience, key effects within an agent-based
interaction system can already be observed with much smaller numbers. Usually,
only a 'critical mass' is needed. This is certainly an area that requires further
investigation, which we currently focus on. Using evolutionary computation tech-
niques, we also explore the effects of different control parameter settings, as well
as how changes in the set of agent interaction rules influence the overall system
behaviour. Being able to easily change agent interaction rules and the types of
agents makes models of complex adaptive systems useful for large-scale scientific
exploration.
Currently, we only have incorporated some of the earlier and basic theories
of how immune system processes might work. Now that we have a flexible and
powerful simulation infrastructure in place, calibrating and validating our mod-
els as well as including more of the recently proposed models is one of our next
steps. We are also expanding our simulations to demonstrate (and help stu-
dents to investigate) why the generation of effective vaccines is dicult and how
spontaneous auto-immunity emerges.
Up-to-date details about our latest immune system model and other agent-
based simulation examples, which are investigated in our
Evolutionary & Swarm
Design Lab
can be found at:
http://www.swarm-design.org
.
Acknowledgements
Financial support for this research is provided by NSERC, the Natural Sciences
and Engineering Research Council of Canada.
We thank Ian Burleigh for creating VIGO::3D, a C++ library for multi-agent
simulation and visualization in 3D space [29], on which the
IMMS:VIGO::3D
system is built. VIGO is an open source project available at:
http://sourceforge.net/projects/vigo
.
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