Much of artificial intelligence research is focused on devising optimal solutions for challenging and well-defined but highly constrained problems. However, as we begin creating autonomous agents to operate in the rich environments of modern videogames and computer simulations, it becomes important to devise agent behaviors that display the visible attributes of intelligence, rather than simply performing optimally. Such visibly intelligent behavior is difficult to specify with rules or characterize in terms of quantifiable objective functions, but it is possible to utilize human intuitions to directly guide a learning system toward the desired sorts of behavior. Policy induction from human-generated examples is a promising approach to training such agents. In this paper, such a method is developed and tested using Lamarckian neuroevolution. Artificial neural networks are evolved to control autonomous agents in a strategy game. The evolution is guided by human-generated examples of play, and the system effectively learns the policies that were used by the player to generate the examples. I.e., the agents learn visibly intelligent behavior. In the future, such methods are likely to play a central role in creating autonomous agents for complex environments, making it possible to generate rich behaviors derived from nothing more formal than the intuitively generated examples of designers, players, or subject-matter experts.

I would like to thank Risto Miikkulainen for his patient support during all stages of this thesis. Risto’s Neural Network class inspired me to start doing research in neuroevolution. Thanks to Ugo Vieruchi for coding JNeat and Julian Togelius for creating the simplerace domain which I used as the base to write the simulations. I also want to thank the Neural Networks group for their valuable suggestions towards this project. The masters program at UT has exposed me to education and research of the highest quality. I am grateful to the CS instructors who led me through the masters program. I found Glenn Downing, Greg Plaxton, Peter Stone, Ray Mooney and Risto Miikkulainen to be inspirational instructors and I cherish my experiences from their classes and lectures. Being a graduate student in CS has slso given me a chance to work on class projects in inter-disciplinary fields like Bioinformatics. I consider myself lucky to have worked with Andrew Ellington and Edward Marcotte at the Institute of

In recent years, several games have presented non-linear gameplay systems; they are also known as sandbox games. Players are offered big, open, full of life worlds where they have a high degree of freedom to choose what they want to do to progress through the game. Multi-agent systems can help providing game designers with the means to achieve their creative visions and build more complex environments. 1