Robust learning in expert networks: a comparative analysis

Abstract

Human experts as well as 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 article extends concepts from Multi-agent Reinforcement Learning and Active Learning to referral networks for distributed learning in referral networks. Among a wide array of algorithms evaluated, Distributed Interval Estimation Learning (DIEL), based on Interval Estimation Learning, was found to be superior for learning appropriate referral choices, compared to 𝜖-Greedy, Q-learning, Thompson Sampling and Upper Confidence Bound (UCB) methods. In addition to a synthetic data set, we compare the performance of the stronger learning-to-refer algorithms on a referral network of high-performance Stochastic Local Search (SLS) SAT solvers where expertise does not obey any known parameterized distribution. An evaluation of overall network performance and a robustness analysis is conducted across the learning algorithms, with an emphasis on capacity constraints and evolving networks, where experts with known expertise drop off and new experts of unknown performance enter — situations that arise in real-world scenarios but were heretofore ignored.