This paper analyzes an approach at evolving intelligent agents to work together to perform and accomplish a common task in a role-playing video game environment. A platform was created that allows research into the capabilities of automated neuro-evolving agents in team (party) environments to work together to defeat a common enemy or perform a common task. This research focuses on a centralized methodology to accomplish this. All party members share a common agent, or brain. Party members are given a set of qualities that contain information that either a player or a computer enemy would need in a MUD or MMORPG combat situation. These qualities are then used by the centralized agent to make decisions for each party member to perform their tasks. This shared agent is then rewarded based on the performance of the party as a whole. This research looks into the effectiveness and limitations of that centralized agent in this environment.

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Technical Report HR-10-03, Department of Computer Science, The University of Texas at Austin.

@techreport{bush:ugthesis10, title={An Analysis of Automated Decision Making Methodologies in Role Playing Video Games: Centralized Approach}, author={Christopher Bush}, number={HR-10-03}, school={Department of Computer Science, The University of Texas at Austin}, institution={Department of Computer Science, The University of Texas at Austin}, type={Undergraduate Honors Thesis}, url="http://www.cs.utexas.edu/users/ai-lab?bush:ugthesis10", year={2010} }