@conference {1502310,
	title = {Every team deserves a second chance: Identifying when things go wrong },
	booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015)},
	year = {2015},
	abstract = {Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance
in finding the correct answer to complex problems. We present a
novel benefit of voting, that has not been observed before: 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 theoretical explanation of why our prediction
method works. Further, contrary to what would be expected based
on a simpler explanation using classical voting models, we argue
that we can make accurate predictions irrespective of the strength
(i.e., performance) of the teams, and that in fact, the prediction can
work better for diverse teams composed of different agents than
uniform teams made of copies of the best agent. We perform experiments in the Computer Go domain, where we obtain a high
accuracy in predicting the final outcome of the games. We analyze
the prediction accuracy for three different teams with different levels of diversity and strength, and we show that the prediction works
significantly better for a diverse team. Since our approach is domain independent, it can be easily applied to a variety of domains.},
	author = {V. Nagarajan and L. S. Marcolino and M. Tambe}
}
