EAGER: Distributed Learning in Expert Referral Networks

当你不知道该问谁的时候你会问谁？在某些情况下，这可能被认为是一个修辞问题，但这是转诊网络的“存在理由”。如果一个人必须解决一个问题，但缺乏如何解决它的知识，他或她问的人谁可以提供一个解决方案，或可能知道别人谁可以提供解决方案。转介网络对于专业成功非常有用，例如在咨询公司，医疗保健组织（例如将患者转介给医学专家）或跨学科研究工作中。 基于人工智能的智能代理的出现，通常具有狭窄的专业知识，可以创建基于代理或混合的人类和代理推荐网络，但增加了推荐过程的复杂性。 为了驯服这种复杂性，新的研究解决了在分布式环境中学习参考的问题。 每个专家都学会更好地估计网络中其他专家的专业知识，无论是人类还是基于AI的代理，因此整个网络的准确性越来越高。学习参考方法对于逐渐的专业知识变化（例如专家学习更好地执行）或网络中的变化（例如有经验的专家退休和/或一个或多个新的但经验较少的专家加入）是鲁棒的。研究开始于修改强化学习的方法，例如成功的区间估计学习方法，将其扩展到分布式神经网络设置。初步实验结果表明，分布式区间阈值学习能有效地提高经验累积推荐的准确性，且优于Q学习和贪婪选择最知名专家等方法。 该研究将解决转介网络拓扑结构变化的鲁棒性问题，信息先验和主动技能广告的好处，由个别专家向他们的同行，以及其他相关方面放松最初的限制性假设，以解决真实的转介网络的情况。除了建立这一新的分布式学习路线外，EAGER还将生成对专家网络学习领域的进一步研究有用的数据集，并使其可用。

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.