The UT^2 bot, which had a humanness rating of 27.2727% in BotPrize 2010, is based on two core ideas: (1) multiobjective neuroevolution is used to learn skilled combat behavior, but filters on the available combat actions ensure that the behavior is still human-like despite being evolved for performance, and (2) a database of traces of human play is used to help the bot get unstuck when its navigation capabilities fail. Several changes have recently been made to UT^2: Extra input features have been provided to the bot to help it evolve better combat behavior, the role of human traces in the navigation of the bot has been expanded, and an extra control module has been added which encourages the bot to observe other players the way a human would, rather than simply battle them. These changes should make UT^2 act more human-like in this year's BotPrize competition.

PDF

In Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG 2011), 329--336, Seoul, South Korea, September 2011. IEEE.

@inproceedings{schrum:cig11competition, title={UT^2: Human-like Behavior via Neuroevolution of Combat Behavior and Replay of Human Traces}, author={Jacob Schrum and Igor V. Karpov and Risto Miikkulainen}, booktitle={Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG 2011)}, month={September}, address={Seoul, South Korea}, publisher={IEEE}, pages={329--336}, url="http://nn.cs.utexas.edu/?schrum:botprize2011", year={2011} }

Slides (PPT)