Leveraging Human Creativity with Machine Discovery
Active from 2008 - 2010
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.

Risto Miikkulainen Faculty risto [at] cs utexas edu
Igor V. Karpov Masters Alumni ikarpov [at] gmail com
Vinod Valsalam Ph.D. Alumni vkv [at] alumni utexas net
Evaluating team behaviors constructed with human-guided machine learning 2015
Igor V. Karpov, Leif M. Johnson and Risto Miikkulainen, To Appear In Proceedings of the IEEE Conference on Computational Intelligence in Games, August 31-July 2 2015.
Believable Bot Navigation via Playback of Human Traces 2012
Igor V. Karpov, Jacob Schrum, Risto Miikkulainen, In Believable Bots, Philip F. Hingston (Eds.), pp. 151--170 2012. Springer Berlin Heidelberg.
Human-Assisted Neuroevolution Through Shaping, Advice and Examples 2011
Igor V. Karpov, Vinod K. Valsalam and Risto Miikkulainen, In Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference (GECCO 2011), Dublin, Ireland, July 2011.
Learning Dynamic Obstacle Avoidance for a Robot Arm Using Neuroevolution 2009
Thomas D'Silva, Risto Miikkulainen, Neural Processing Letters (2009).
Constructing Complex NPC Behavior via Multi-Objective Neuroevolution 2008
Jacob Schrum and Risto Miikkulainen, In Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2008), pp. 108-113, Stanford, California 2008.
OpenNERO OpenNERO is a general research and education platform for artificial intelligence. The platform is based on a simulatio... 2010

rtNEAT C++ The rtNEAT package contains source code implementing the real-time NeuroEvolution of Augmenting Topologies method. In ad... 2006