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Autonomous Agents and Multi-Agent Systems
Erschienen in: Autonomous Agents and Multi-Agent Systems 6/2014

01.11.2014

Automated agents for reward determination for human work in crowdsourcing applications

verfasst von: Amos Azaria, Yonatan Aumann, Sarit Kraus

Erschienen in: Autonomous Agents and Multi-Agent Systems | Ausgabe 6/2014

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Abstract

Crowdsourcing applications frequently employ many individual workers, each performing a small amount of work. In such settings, individually determining the reward for each assignment and worker may seem economically beneficial, but is inapplicable if manually performed. We thus consider the problem of designing automated agents for automatic reward determination and negotiation in such settings. We formally describe this problem and show that it is NP-hard. We therefore present two automated agents for the problem, based on two different models of human behavior. The first, the Reservation Price Based Agent (RPBA), is based on the concept of a RP, and the second, the No Bargaining Agent (NBA) which tries to avoid any negotiation. The performance of the agents is tested in extensive experiments with real human subjects, where both NBA and RPBA outperform strategies developed by human experts.
Vorheriger Artikel Trust-oriented buyer strategies for seller reporting and selection in competitive electronic marketplaces
Nächster Artikel Multiagent resource allocation with sharable items

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Fußnoten
1
In most cases the assignments are not completely identical, but they are sufficiently similar so that the associated reward is identical (e.g. checking if a given link is stale).
 
2
We consider only types where \(d_t\) can be represented by a description which is polynomial in the size of the game.
 
3
For a comparison between AMT and other recruitment methods see [42].
 
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Metadaten
Titel
Automated agents for reward determination for human work in crowdsourcing applications
verfasst von
Amos Azaria
Yonatan Aumann
Sarit Kraus
Publikationsdatum
01.11.2014
Verlag
Springer US
Erschienen in
Autonomous Agents and Multi-Agent Systems / Ausgabe 6/2014
Print ISSN: 1387-2532
Elektronische ISSN: 1573-7454
DOI
https://doi.org/10.1007/s10458-013-9244-y

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