EAGER: Distributed Learning in Expert Referral Networks

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Whom do you ask when you don't know whom to ask? That may be considered a rhetorical question in some contexts, but it is the "raison d'être" for referral networks. If a person must address a problem, but lacks the knowledge of how to solve it, he or she asks someone who may either provide a solution, or may know someone else who might provide the solution. Referral networks are very useful for professional success, such as in consulting companies, health-care organizations (e.g. referral of patients to medical specialists) or interdisciplinary research endeavors. The advent of AI-based intelligent agents, who typically have narrow expertise, enables the creation of agent-based or mixed human-and-agent referral networks, but adds complexity to the referral process. In order to tame this complexity, the new research addresses learning to refer in a distributed setting. Each expert learns to better estimate the expertise of other experts in the network, whether human or AI-based agents, and thus overall network refers with increasing accuracy. The learning-to-refer methods are robust with respect to gradual expertise change (e.g. experts learn to perform better) or changes in the network (e.g. an experienced expert retires and/or one or more new but less experienced experts join). The research starts by modifying methods from reinforcement learning, such as the successful interval-estimation learning approach, extending them to the distributed referral-network setting. Preliminary results show that distributed interval threshold learning is effective in improving the accuracy of referrals with accrued experienced, and performs better than other approaches such as Q-learning or greedy selection of best-known expert. The research will address issues of robustness to changes in the referral network topology, benefits of informative priors and proactive skill advertisement by individual experts to their peers, and other related aspects relaxing the initial restrictive assumptions in order to address real referral-network scenarios. In addition to establishing this new line of distributed learning, this EAGER will generate data sets useful for further research in the area of expertise-network learning and make them available.