TY - GEN

T1 - Every team deserves a second chance

T2 - Identifying when things go wrong

AU - Nagarajan, Vaishnavh

AU - Soriano Marcolino, Leandro

AU - Tambe, Milind

PY - 2015/5

Y1 - 2015/5

N2 - 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 arguethat 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.

AB - 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 arguethat 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.

M3 - Conference contribution/Paper

SP - 695

EP - 703

BT - Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015)

ER -