Abstract
Distributed learning in expert referral networks is a new Active Learning paradigm where experts—humans or automated agents—solve problems if they can or refer said problems to others with more appropriate expertise. Recent work augmented the basic learning-to-refer method with proactive skill posting, where experts may report their top skills to their colleagues, and proposed a modified algorithm, proactive-DIEL (Distributed Interval Estimation Learning), that takes advantage of such one-time posting instead of using an uninformed prior. This work extends the method in three main directions: (1) Proactive-DIEL is shown to work on a referral network of automated agents, namely SAT solvers, (2) Proactive-DIEL’s reward mechanism is extended to another referral-learning algorithm, \(\epsilon \)-Greedy, with some appropriate modifications. (3) The method is shown robust with respect to evolving networks where experts join or drop off, requiring the learning method to recover referral expertise. In all cases the proposed method exhibits superiority to the state of the art.
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Notes
- 1.
Note that the choice to use 50-iteration bursts is purely for visualization reasons and our results do not change qualitatively when we consider similar changes distributed across the entire course of the simulation. We also ran experiments with a large one-time network change from which both DIEL and proactive-DIEL recovered well.
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This research is partially funded by the National Science Foundation grant EAGER-1649225.
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KhudaBukhsh, A.R., Carbonell, J.G., Jansen, P.J. (2017). Proactive-DIEL in Evolving Referral Networks. In: Criado Pacheco, N., Carrascosa, C., Osman, N., Julián Inglada, V. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2016 2016. Lecture Notes in Computer Science(), vol 10207. Springer, Cham. https://doi.org/10.1007/978-3-319-59294-7_13
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