A Quality Assuring Multi-armed Bandit Crowdsourcing Mechanism with Incentive Compatible Learning

被引：0

作者：

Jain, Shweta ^{[1
]}

Gujar, Sujit ^{[2
]}

Zoeter, Onno ^{[3
]}

Narahari, Y. ^{[1
]}

机构：

[1] IISc Bangalore, Bengaluru, India

[2] EPFL Lausanne, Lausanne, Switzerland

[3] XRCE Meylan, Meylan, France

来源：

AAMAS'14: PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS | 2014年

关键词：

Mechanism Design; Multi-armed Bandit; Crowdsourcing;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We develop a novel multi-armed bandit (MAB) mechanism for the problem of selecting a subset of crowd workers to achieve an assured accuracy for each binary labelling task in a cost optimal way. This problem is challenging because workers have unknown qualities and strategic costs.

引用

页码：1609 / 1610

页数：2