Agent-based Players for a First-person Entertainment-based Real-time Artificial Environment

The necessity for improved players and opponents in firstperson entertainment-based real-time artificial environments has inspired our research into artificial game players. We employed the approach of creating agent-based players, one using simple reflex-action based components and another based on a back propagation neural network, to interact in a modified Quake II environment. Our evaluation of these agents utilizes two metrics, a statistical measure and graph similarity based on clustering from player performance traces against interactive feature points in the environments--both comparing the agents to a sample of 20 human players. The reflex-based agent was able to complete 29 of 100 test levels with 73.1% within statistical humanperformance levels and 15.4% similar to the human players, while the BPNN-based agent was unable to complete any levels. These results, including some additional findings that reveal the potential for a combinational approach, are inspiring an architec...

• Type: Conference
• Year: 2004
• Where: FLAIRS
• Authors: G. Michael Youngblood, Lawrence B. Holder
• Keywords: Artificial Game Players, Artificial Intelligence, FLAIRS 2004, Human Players, Real-time Artificial Environments