It's not surprizing that CAS study also concerns neural networks. Quite a number of publications concerns a kind of multi-agent systems, where each agent is driven by its neural network, which in most cases evolves during the experiment. The examples are publications by the evolutionary robotics related groups members. Thus one can say that CAS utilizes neuroevolutionary algorithms to study system-level behaviour. This is possible because evolving ANNs tend to create more complex agents' behaviours, which leads to the more complex interactions. Good example here is an evolving predator-prey system where both predator and prey movement was driven by their evolved ANNs. As time passed several strategies had been tested by both sides and each following strategy was 'invented' to compete the current opposing strategy. Here is an illustrative picture from Floreano D, Keller L (2010) Evolution of Adaptive Behaviour in Robots by Means of Darwinian Selection. PLoS Biol 8(1): e1000292. doi:10.1371/journal.pbio.1000292
But this is about how NE can help to CAS. And is there a 'feedback'? How can CAS contribute to the neuroevolution? I think that since ANN is a complex adaptive system itself the CAS study can be helpful answering the following questions:
- how to evolve irregular-structured ANNs, which possess some desired system-level properties *and* are robust and reliable.
- how can neural modules, consisting of multiple nodes, be connected and arranged to perform some specified task.
- what are the ways and principles to dynamically change ANN structure and interconnections between modules and nodes to provide adaptivity in changing environment.
