Coevolution of Role-Based Cooperation in Multi-Agent Systems (2010)
In tasks such as pursuit and evasion, multiple agents need to coordinate their behavior to achieve a common goal. An interesting question is, how can such behavior be best evolved? A powerful approach is to control the agents with neural networks, coevolve them in separate subpopulations, and test them together in the common task. In this paper, such a method, called Multi-Agent ESP (Enforced SubPopulations), is proposed and demonstrated in a prey-capture task. First, the approach is shown to be more efficient than evolving a single central controller for all agents. Second, cooperation is found to be most efficient through stigmergy, i.e. through role-based responses to the environment, rather than communication between the agents. Together these results suggest that role-based cooperation is an effective strategy in certain multi-agent tasks.
IEEE Transactions on Autonomous Mental Development, Vol. 1 (2010), pp. 170--186.

Risto Miikkulainen Faculty risto [at] cs utexas edu
Chern Han Yong Masters Alumni cherny [at] nus edu sg
ESP C++ The ESP package contains the source code for the Enforced Sup-Populations system written in C++. ESP is an extension t... 2000