Multiagent Learning through Neuroevolution (2012)
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. This paper reviews recent progress in these three areas, and suggests avenues for future work.
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In Advances in Computational Intelligence, J. Liu et al. (Eds.), Vol. LNCS 7311, pp. 24-46, Berlin, Heidelberg: 2012. Springer.
Bibtex:

Eliana Feasley Formerly affiliated Ph.D. Student elie [at] cs utexas edu
Igor V. Karpov Ph.D. Student ikarpov [at] gmail com
Risto Miikkulainen Faculty risto [at] cs utexas edu
Padmini Rajagopalan Ph.D. Student padmini [at] cs utexas edu
Aditya Rawal Ph.D. Student aditya [at] cs utexas edu
Wesley Tansey Ph.D. Student tansey [at] cs utexas edu
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