Autonomous Design of Modular Intelligent Systems

We propose our original system capable of autonomous design of general-purpose complex modular hybrid systems. The resulting hybrid systems will be able to employ various techniques of learning, decision-making, prediction etc. Presented topic is from Artificial Life domain, but contributes also to fields such as Artificial Intelligence, Biology, Computational Neuroscience, Ethology, Cybernetics and potentially into many other aspects of research. The autonomous design is implemented as an optimization of system topology with respect to given problem. The principle of design is based on modified neuro-evolution and can be compared to modular neural networks. One of the main requirements is standardization of communication between very different subsystems. Here, each subsystem - module implements arbitrary algorithm and is treated as a Multiple-Input Multiple-Output subsystem. First, the design of simulator used is described, then the basic principle of hybrid networks is explained with it benefits and drawbacks. Finally, simple example is mentioned.