Measuring Progress in Coevolutionary Competition

Evolution, as trial-and-error based learning methods, usually relies on the repeatability of an experience: Different behavioral alternatives are tested and compared with each other. But agents acting on real environments may not be able to choose which experience to live. Instead, the environment provides varying initial conditions for each trial. In competitive games for example, it is difficult to compare players with each other if they are not able to choose their opponents. Here we describe a statistics based approach to solving this problem, developed in the context of the Tron system, a coevolutionary experiment that matches humans against agents on a simple video game. We are now able to show, among the results, that the complex interactions led the artificial agents to evolve towards higher proficiency, while at the same time, individual humans learned as they gained experience interacting with the system.