Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Every team makes mistakes, in large action spaces
AU - Soriano Marcolino, Leandro
AU - Nagarajan, Vaishnavh
AU - Tambe, Milind
PY - 2015
Y1 - 2015
N2 - Voting is applied to better estimate an optimal answer to complex problems in many domains. Werecently presented 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 whether it will be successful or not in problem-solving. Our prediction technique is completely domain independent, and it can be executed at any time during problem solving. In this paper we present a novel result about our technique: we show that the prediction quality increases with the size of the action space. We present a theoreticalexplanation for such phenomenon, and experiments in Computer Go with a variety of board sizes.
AB - Voting is applied to better estimate an optimal answer to complex problems in many domains. Werecently presented 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 whether it will be successful or not in problem-solving. Our prediction technique is completely domain independent, and it can be executed at any time during problem solving. In this paper we present a novel result about our technique: we show that the prediction quality increases with the size of the action space. We present a theoreticalexplanation for such phenomenon, and experiments in Computer Go with a variety of board sizes.
M3 - Conference contribution/Paper
BT - 9th Multidisciplinary Workshop on Advances in Preference Handling (M-PREF 2015)
ER -