构建智能RPA的机器学习融合分类体系，助力复杂任务自动化
A Nascent Taxonomy of Machine Learning in Intelligent Robotic Process Automation
- 从集成与交互双维度建立智能RPA分类框架
- 提出涵盖8个维度的元特征体系，系统梳理技术关联
- 适合研究RPA智能化演进的开发者与决策者参考
机器人流程自动化（RPA）是一种通过软件机器人在图形界面层模拟用户操作的轻量级业务流程自动化方法。尽管其在规则明确、结构清晰的任务中成本低且见效快，但其符号化特性在处理更复杂的任务时存在局限。引入机器学习可提升RPA的智能水平，拓展自动化范围。本文通过文献综述，探索RPA与机器学习的关联，构建了智能RPA的分类体系。该体系包含两大元特征：RPA-ML集成与RPA-ML交互，共同构成八个维度：架构与生态、能力、数据基础、智能水平、集成技术深度，以及部署环境、生命周期阶段和人机关系。
原文摘要 · Abstract (English)
Robotic process automation (RPA) is a lightweight approach to automating business processes using software robots that emulate user actions at the graphical user interface level. While RPA has gained popularity for its cost-effective and timely automation of rule-based, well-structured tasks, its symbolic nature has inherent limitations when approaching more complex tasks currently performed by human agents. Machine learning concepts enabling intelligent RPA provide an opportunity to broaden the range of automatable tasks. In this paper, we conduct a literature review to explore the connections between RPA and machine learning and organize the joint concept intelligent RPA into a taxonomy. Our taxonomy comprises the two meta-characteristics RPA-ML integration and RPA-ML interaction. Together, they comprise eight dimensions: architecture and ecosystem, capabilities, data basis, intelligence level, and technical depth of integration as well as deployment environment, lifecycle phase, and user-robot relation.
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