The Library
BudgetFix : budget limited crowdsourcing for interdependent task allocation with quality guarantees
Tools
Tools
Tools
Tran-Thanh, Long, Huynh, Trung Dong, Rosenfeld, Avi, Ramchurn, Sarvapali and Jennings, Nicholas R. (2014) BudgetFix : budget limited crowdsourcing for interdependent task allocation with quality guarantees. In: AAMAS '14: International conference on Autonomous Agents and Multi-Agent Systems, Paris, France, 5-9 May 2014. Published in: AAMAS '14: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems pp. 477-484. ISSN 9781450327381.
|
PDF
WRAP-BudgetFix-budget-limited-crowdsourcing-interdependent-task-allocation-quality-guarantees-Tran-Thanh-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1495Kb) | Preview |
Official URL: https://dl.acm.org/doi/10.5555/2615731.2615809
Abstract
Crowdsourcing is a multi-agent task allocation paradigm that involves up to millions of workers, of varying reliability and availability, performing large numbers of micro-tasks. A key challenge is to crowdsource, at minimal cost and with predictable accuracy, complex tasks that involve different types of interdependent microtasks structured into complex workflows. In this paper, we propose the first crowdsourcing algorithm that solves this problem. Our algorithm, called BudgetFix, determines the number of interdependent microtasks and the price to pay for each task given budget constraints. Moreover, BudgetFix provides quality guarantees on the accuracy of the output of each phase of a given workflow. BudgetFix is empirically evaluated on a well known crowdsourcingbased text correction workflow using Amazon Mechanical Turk, and is shown that BudgetFix can provide similar accuracy, compared to the state-of-the-art algorithm for this workflow, but is on average 32% cheaper.
| Item Type: | Conference Item (Paper) | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | H Social Sciences > HJ Public Finance Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
||||||
| Divisions: | Faculty of Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Crowdsourcing, Budget -- Mathematical models, Organizational change, Human computation, Artificial intelligence -- Computer programs, Computer networks -- Quality control | ||||||
| Journal or Publication Title: | AAMAS '14: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems | ||||||
| Publisher: | IFAAMAS | ||||||
| ISSN: | 9781450327381 | ||||||
| Official Date: | May 2014 | ||||||
| Dates: |
|
||||||
| Date of first compliant deposit: | 22 July 2020 | ||||||
| Page Range: | pp. 477-484 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Publisher Statement: | © ACM, 2014 This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Tran-Thanh, Long, Huynh, Trung Dong, Rosenfeld, Avi, Ramchurn, Sarvapali and Jennings, Nicholas R. (2014) BudgetFix : budget limited crowdsourcing for interdependent task allocation with quality guarantees. In: AAMAS '14: International conference on Autonomous Agents and Multi-Agent Systems, Paris, France, 5-9 May 2014. Published in: AAMAS '14: Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems pp. 477-484. ISSN 9781450327381 | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| RIOXX Funder/Project Grant: |
|
||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | AAMAS '14: International conference on Autonomous Agents and Multi-Agent Systems | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Paris, France | ||||||
| Date(s) of Event: | 5-9 May 2014 | ||||||
| Related URLs: | |||||||
| Open Access Version: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
