Information systems increasingly leverage artificial intelligence (AI) and
machine learning (ML) to generate value from vast amounts of data. However, ML
models are imperfect and can generate incorrect classifications. Hence,
human-in-the-loop (HITL) extensions to ML models add a human review for
instances that are difficult to classify. This study argues that continuously
relying on human experts to handle difficult model classifications leads to a
strong increase in human effort, which strains limited resources. To address
this issue, we propose a hybrid system that creates artificial experts that
learn to classify data instances from unknown classes previously reviewed by
human experts. Our hybrid system assesses which artificial expert is suitable
for classifying an instance from an unknown class and automatically assigns it.
Over time, this reduces human effort and increases the efficiency of the
system. Our experiments demonstrate that our approach outperforms traditional
HITL systems for several benchmarks on image classification.

人工智能和机器学习在信息系统中发挥了重要作用，然而机器学习模型不完美并且可能产生错误分类。因此，本研究提出了一种混合系统，利用人工专家对难以分类的实例进行审核，并创造了人工专家来学习对之前由人工专家审核过的未知类别数据实例进行分类。实验证明，这种方法在图像分类的几个基准测试中优于传统的人机合作系统。

提高人机协作系统的效率：将人工智能加入人类专家

Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial  to Human Experts

The prospect of combining human operators and virtual agents (bots) into an
effective hybrid system that provides proper customer service to clients is
promising yet challenging. The hybrid system decreases the customers'
frustration when bots are unable to provide appropriate service and increases
their satisfaction when they prefer to interact with human operators.
Furthermore, we show that it is possible to decrease the cost and efforts of
building and maintaining such virtual agents by enabling the virtual agent to
incrementally learn from the human operators. We employ queuing theory to
identify the key parameters that govern the behavior and efficiency of such
hybrid systems and determine the main parameters that should be optimized in
order to improve the service. We formally prove, and demonstrate in extensive
simulations and in a user study, that with the proper choice of parameters,
such hybrid systems are able to increase the number of served clients while
simultaneously decreasing their expected waiting time and increasing
satisfaction.

利用排队理论，将人工操作员与虚拟代理程序（机器人）组合为一种混合系统，可提供客户服务，同时减少顾客沮丧，增加满意度。

高效的客户服务：人工操作员与虚拟代理的结合

Efficient Customer Service Combining Human Operators and Virtual Agents

This work investigates the task-oriented dialogue problem in mixed-domain
settings. We study the effect of alternating between different domains in
sequences of dialogue turns using two related state-of-the-art dialogue
systems. We first show that a specialized state tracking component in multiple
domains plays an important role and gives better results than an end-to-end
task-oriented dialogue system. We then propose a hybrid system which is able to
improve the belief tracking accuracy of about 28% of average absolute point on
a standard multi-domain dialogue dataset. These experimental results give some
useful insights for improving our commercial chatbot platform FPT.AI, which is
currently deployed for many practical chatbot applications.

研究了跨领域环境下的任务导向对话问题，并探讨了通过使用特定的状态监测组件和混合式系统来提高任务信念跟踪准确率的方法。