Distributed Learning in Expert Referral Networks

Human experts or autonomous agents in a referral network must decide whether to accept a task or refer to a more appropriate expert, and if so to whom. In order for the referral network to improve over time, the experts must learn to estimate the topical expertise of other experts. This paper extends concepts from Reinforcement Learning and Active Learning to referral networks, to learn how to refer at the network level, based on the proposed distributed interval estimation learning (DIEL) algorithm. Diverse Monte Carlo simulations reveal that DIEL improves network performance significantly over both greedy and Q-learning baselines [3], approaching optimal given enough data.