Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in machine learning. However, the potential of voting has been explored only in improving the ability of finding the correct answer to a complex problem. In this paper we present a novel benefit in voting, that has not been observed before: we show that we can use the voting patterns to assess the performance of a team and predict their final outcome.

This prediction can be executed at any moment during problem-solving and it is completely domain independent. We present a preliminary theoretical explanation of why our prediction method works, where we show that the accuracy is better for diverse teams composed
by different agents than for uniform teams made of copies of the same agent. We also perform experiments in the Computer Go domain, where we show that we can obtain a high accuracy in predicting the final outcome of the games. We analyze the prediction accuracy for 3 different teams, and we show that the prediction works significantly better for a diverse team. Since our
approach is completely domain independent, it can be easily applied to a variety of domains, such as the video games in the Arcade Learning Environment.