Risto Miikkilainen - Multiagent Learning Through Neuroevolution
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Risto Miikkilainen - Multiagent Learning Through Neuroevolution
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Abstract: Neuroevolution is a promising approach for constructing intelligent agents in many complex tasks such as games, robotics, and decision making. It is also well suited for evolving team behavior for many multiagent tasks. However, new challenges and opportunities emerge in such tasks, including facilitating cooperation through reward sharing and communication, accelerating evolution through social learning, and measuring how good the resulting solutions are. In this talk I will review recent progress in these three areas, and suggest avenues for future work. Biography: Risto Miikkulainen is a Professor of Computer Sciences at the University of Texas at Austin. He received an M.S. in Engineering from the Helsinki University of Technology, Finland, in 1986, and a Ph.D. in Computer Science from UCLA in 1990. His current research focuses on methods and applications of neuroevolution, as well as models of natural language processing, and self-organization of the visual cortex; he is an author of over 250 articles in these research areas. He is currently on the Board of Governors of the Neural Network Society, and an action editor of IEEE Transactions on Computational Intelligence and AI in Games and IEEE Transactions on Autonomous Mental Development.
- Published on
- August 1, 2012
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