Although biological inspiration has long played a role in engineering artificial systems, the flow of ideas and tools in the other direction has been increasing. Neural networks were inspired by brain function and in turn are used to model it. So too with reinforcement learning. Genetic algorithms are inspired by evolutionary processes and now form the basis of models used to investigate evolutionary theory. Algorithms derived from social insect research find applications in engineering, while computer models of insect colonies advance understanding of the decision-making capabilities of these natural systems.
Artificial systems are typically engineered in (or, rather, on top of) silicon, but recent work on DNA and cellular computing has blurred the lines between the implementation details of artificial and natural systems.
The convention theme for AISB'06 reflects this rich interaction between the study of adaptation in the artificial and the biological. AISB'06 is organised by the Machine Learning and Biological Computation Group in the Department of Computer Science, University of Bristol, and by the Society for the Study of Artificial Intelligence and Simulation of Behaviour.
The original call can be viewed here.