2019年度 人工知能学会全国大会（第33回）
2019年6月4日〜6月7日朱鷺メッセ 新潟コンベンションセンター
2019年度 人工知能学会全国大会（第33回）
2019年6月4日〜6月7日朱鷺メッセ 新潟コンベンションセンター
[2O5-E-3-01]Improving the Accuracy of the Collective Prediction by Maintaining the Diversity of Opinions: Preliminary Report
〇Rui Chen1, Shigeo Matsubara1(1. Kyoto University)
This study aims to improve prediction accuracy by fostering diversity of opinions. We take an approach to give incentive to agents and induce diverse opinions and focus the minority reward system. The previous study assumes that the number of agents is sufficiently large, but the number of agents may be small in real-world situations. We show that the minority reward system is not necessarily efficient if the number of agents is small such as 100. To overcome this drawback, we propose a method to improve the performance by tuning the threshold for determining the minority and show the preliminary result of the evaluation.