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Endorsement in Referral Networks

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Multi-Agent Systems (EUMAS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11450))

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Abstract

Referral networks is an emerging research area in the intersection of Active Learning and Multi-Agent Systems where experts—humans or automated agents—can redirect difficult instances (tasks or queries) to appropriate colleagues. Learning-to-refer involves estimating topic-conditioned skills of colleagues connected through a referral network for effective referrals. Proactive skill posting is a learning setting where experts are allowed a one-time local network advertisement of a subset of their top skills. The learning challenge is exploiting partially available (potentially noisy) self-skill estimates, including adversarial strategic lying to attract unwarranted referrals. In this paper, we introduce the notion of endorsement typically found in professional networks where one colleague endorses another on particular topic(s). We first augment proactive skill posting with endorsements and propose modifications to existing algorithms to take advantage of such endorsements, penalizing subsequent referrals to agents with bogus skill reporting. Our results indicate that truthful endorsements improve performance as they act as an additional cushion to early failures of strong experts. When combined with truthful endorsements, extensive empirical evaluations indicate performance improvement in proactive-DIEL and \(\epsilon \)-Greedy in both market-aware and market-agnostic skill posting setting while retaining desirable properties like tolerance to noisy self-skill estimates and strategic lying.

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Notes

  1. 1.

    In the original paper [2], proactive-DIEL is referred to as proactive-DIEL\(_t\) because it uses a trust-based mechanism for incentive compatibility. Since both proactive-DIEL\(_t\) and proactive-DIEL\(_\varDelta \) use the same penalty mechanism, we drop the t subscript from market-agnostic algorithms and use the \(\varDelta \) subscript to indicate market-awareness.

  2. 2.

    The data sets along with detailed instructions, algorithm implementations, and parameter configurations are publicly available at https://www.cs.cmu.edu/~akhudabu/referral-networks.html.

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Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, USA

    Ashiqur R. KhudaBukhsh & Jaime G. Carbonell

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  1. Ashiqur R. KhudaBukhsh
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  2. Jaime G. Carbonell
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Correspondence to Ashiqur R. KhudaBukhsh .

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Editors and Affiliations

  1. University of Bergen, Bergen, Norway

    Marija Slavkovik

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KhudaBukhsh, A.R., Carbonell, J.G. (2019). Endorsement in Referral Networks. In: Slavkovik, M. (eds) Multi-Agent Systems. EUMAS 2018. Lecture Notes in Computer Science(), vol 11450. Springer, Cham. https://doi.org/10.1007/978-3-030-14174-5_12

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  • DOI: https://doi.org/10.1007/978-3-030-14174-5_12

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