A challenge in machine learning is to devise methods that allow incorporating human insight into the automated learning process. Current learning methods employ representations that make it difficult to encode simplification and specific examples, and learning is based on random exploration that is difficult to direct.

NEAT is a learning system where the learned decision policy is represented in neural networks and learned through evolutionary optimization, i.e. genetic algorithms. NEAT evolves network structure as well as weights, which makes it possible in principle to incorporate human guidance in three ways: (1) building a gradually more complex network structure through shaping from simple to more complex tasks, (2) training networks with examples of human behavior, and (3) converting human-designed rules into network structures.

These techniques will be developed and evaluated in the domain of designing complex behaviors for autonomous agents in the OpenNERO 3D simulation environment. In a series of human subject experiments, the solutions designed through human-guided neuroevolution will be compared to those designed by human engineers and to those discovered by neuroevolution alone, verifying that (a) the human-guided approach results in better solutions, and (b) those solutions are more creative.

The result of this project is a machine learning approach will allow engineers to generate creative designs to many real-world sequential decision problems. Applications of this approach will lead to safer and more efficient vehicle, traffic and robotic control, improved process and manufacturing optimization, and more efficient computer and communication systems. It will also make the next generation of video games possible, with characters that exhibit realistic and adaptive behaviors; such technology should lead to more effective educational and training games in the future.

This research is supported by the National Science Foundation under grant IIS-0757479.