Every team deserves a second chance:an extended study on predicting team performance

Soriano Marcolino, Leandro and Lakshminarayanan, Aravind and Nagarajan, Vaishnavh and Tambe, Milind (2017) Every team deserves a second chance:an extended study on predicting team performance. Autonomous Agents and Multi-Agent Systems, 31 (5). pp. 1003-1054.

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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. Hence, it can be used to identify when a team is failing, allowing an operator to take remedial procedures (such as changing team members, the voting rule, or increasing the allocation of resources). We present three main theoretical results: (1) we show a theoretical explanation of why our prediction method works; (2) contrary to what would be expected based on a simpler explanation using classical voting models, we show 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; (3) we show that the quality of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer Go, we obtain high quality predictions about the final outcome of games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and show that the prediction works significantly better for a diverse team. Additionally, we show that our method still works well when trained with games against one adversary, but tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is domain independent, we also present results in Ensemble Learning, where we make online predictions about the performance of a team of classifiers, while they are voting to classify sets of items. We study a set of classical classification algorithms from machine learning, in a data-set of hand-written digits, and we are able to make high-quality predictions about the final performance of two different teams. Since our approach is domain independent, it can be easily applied to a variety of other domains.

Item Type:
Journal Article
Journal or Publication Title:
Autonomous Agents and Multi-Agent Systems
Additional Information:
The final publication is available at Springer via http://dx.doi.org/10.1007/s10458-016-9348-2
Subjects:
ID Code:
81897
Deposited By:
Deposited On:
05 Oct 2016 09:34
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Sep 2020 03:01