Proactive Skill Posting in Referral Networks

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.