Abstract
Distributed learning in expert referral networks is an emerging challenge in the intersection of Active Learning and Multi-Agent Reinforcement Learning, where experts—humans or automated agents—can either solve problems themselves or refer said problems to others with more appropriate expertise. Recent work demonstrated methods that can substantially improve the overall performance of a network and proposed a distributed referral-learning algorithm, DIEL (Distributed Interval Estimation Learning), for learning appropriate referral choices. This paper augments the learning setting with a proactive skill posting step where experts can report some of their top skills to their colleagues. We found that in this new learning setting with meaningful priors, a modified algorithm, proactive-DIEL, performed initially much better and reached its maximum performance sooner than DIEL on the same data set used previously. Empirical evaluations show that the learning algorithm is robust to random noise in an expert’s estimation of her own expertise, and there is little advantage in misreporting skills when the rest of the experts report truthfully, i.e., the algorithm is near Bayesian-Nash incentive-compatible.
References
Babaioff, M., Sharma, Y., Slivkins, A.: Characterizing truthful multi-armed bandit mechanisms. In: Proceedings of the 10th ACM Conference on Electronic Commerce, pp. 79–88. ACM (2009)
Biswas, A., Jain, S., Mandal, D., Narahari, Y.: A truthful budget feasible multi-armed bandit mechanism for crowdsourcing time critical tasks. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, pp. 1101–1109. International Foundation for Autonomous Agents and Multiagent Systems (2015)
Donmez, P., Carbonell, J.G., Schneider, J.: Efficiently learning the accuracy of labeling sources for selective sampling. In: Proceedings of KDD 2009, p. 259 (2009)
Kaelbling, L.P.: Learning in Embedded Systems. MIT Press, Cambridge (1993)
Kaelbling, L.P., Littman, M.L., Moore, A.P.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)
Kautz, H., Selman, B., Milewski, A.: Agent amplified communication, pp. 3–9 (1996)
KhudaBukhsh, A.R., Jansen, P.J., Carbonell, J.G.: Distributed learning in expert referral networks. In: European Conference on Artificial Intelligence (ECAI) 2016, pp. 1620–1621 (2016)
Nallapati, R., Peerreddy, S., Singhal, P.: Skierarchy: extending the power of crowdsourcing using a hierarchy of domain experts, crowd and machine learning. Technical report, DTIC Document (2012)
Tran-Thanh, L., Chapman, A., Rogers, A., Jennings, N.R.: Knapsack based optimal policies for budget-limited multi-armed bandits. arXiv preprint arXiv:1204.1909 (2012)
Tran-Thanh, L., Stein, S., Rogers, A., Jennings, N.R.: Efficient crowdsourcing of unknown experts using multi-armed bandits. In: European Conference on Artificial Intelligence, pp. 768–773 (2012)
Yolum, P., Singh, M.P.: Dynamic communities in referral networks. Web Intell. Agent Syst. 1(2), 105–116 (2003)
Yu, B.: Emergence and evolution of agent-based referral networks. Ph.D. thesis, North Carolina State University (2002)
Yu, B., Singh, M.P.: Searching social networks. In: Proceedings of AAMAS 2003 (2003)
Yu, B., Venkatraman, M., Singh, M.P.: An adaptive social network for information access: theoretical and experimental results. Appl. Artif. Intell. 17, 21–38 (2003)
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KhudaBukhsh, A.R., Carbonell, J.G., Jansen, P.J. (2016). Proactive Skill Posting in Referral Networks. In: Kang, B.H., Bai, Q. (eds) AI 2016: Advances in Artificial Intelligence. AI 2016. Lecture Notes in Computer Science(), vol 9992. Springer, Cham. https://doi.org/10.1007/978-3-319-50127-7_52
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DOI: https://doi.org/10.1007/978-3-319-50127-7_52
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