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2018 | OriginalPaper | Buchkapitel

Incentive Compatible Proactive Skill Posting in Referral Networks

verfasst von : Ashiqur R. KhudaBukhsh, Jaime G. Carbonell, Peter J. Jansen

Erschienen in: Multi-Agent Systems and Agreement Technologies

Verlag: Springer International Publishing

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Abstract

Learning to refer in a network of experts (agents) consists of distributed estimation of other experts’ topic-conditioned skills so as to refer problem instances too difficult for the referring agent to solve. This paper focuses on the cold-start case, where experts post a subset of their top skills to connected agents, and as the results show, improve overall network performance and, in particular, early-learning-phase behavior. The method surpasses state-of-the-art, i.e., proactive-DIEL, by proposing a new mechanism to penalize experts who misreport their skills, and extends the technique to other distributed learning algorithms: proactive-\(\epsilon \)-Greedy, and proactive-Q-Learning. Our proposed new technique exhibits stronger discouragement of strategic lying, both in the limit and finite-horizon empirical analysis. The method is shown robust to noisy self-skill estimates and in evolving networks.

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Vorheriges Kapitel Multiagent Resource Allocation: The Power and Limitations of Bilateral Deals (Extended Abstract)

Nächstes Kapitel Multi-agent Interactions on the Web Through Linked Data Notifications

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Titel: Incentive Compatible Proactive Skill Posting in Referral Networks
verfasst von: Ashiqur R. KhudaBukhsh
Jaime G. Carbonell
Peter J. Jansen
Verlag: Springer International Publishing
Buch: Multi-Agent Systems and Agreement Technologies
Print ISBN: 978-3-030-01712-5

Electronic ISBN: 978-3-030-01713-2

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01713-2_3

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