Autonomous Agent Learning Special Session for the 2011 IEEE Congress on Evolutionary Computation (CEC 2011)
Two areas where autonomous agents are of significant importance are in robotics and interactive video games. Autonomous robots are used for tasks that are too dangerous or impractical for humans to perform. These include exploration in dangerous or remote locations, intelligence gathering, and office or domestic assistance. Agents in interactive video games are used to provide additional players (team members or opponents) to interact with human players. Many of these games are used purely for entertainment, but include games used for training. In either case, having competitive, human like autonomous agents in a game increases the realism and entertainment value. In both autonomous robots and interactive video game agents, learning systems can be significant aspects of their development and enhancement.
This special session aims to discuss autonomous agent learning systems with the primary emphasis on the use of evolutionary computation or reinforcement learning to learn control programs for autonomous robots and interactive video game agents. Topics for contributions include, but may not be limited to the use of reinforcement learning or the varying forms of evolutionary computation to learn controllers for robots, simulations of robots, interactive video game agents, teams of agents, and predator/prey agents.
Special session organizers: Gary Parker, Connecticut College, USA ( snipped-for-privacy@conncoll.edu) Hisashi Handa, Okayama University, Japan ( snipped-for-privacy@sdc.it.okayama- u.ac.jp) Lee Graham, Connecticut College, USA ( snipped-for-privacy@conncoll.edu)
Submission deadline is 28 January 2011: