Analyzing Variable Human Actions for Robotic Process Automation

Robotic Process Automation (RPA) provides a means to automate mundane and repetitive human tasks. Task Mining approaches can be used to discover the actions that humans take to carry out a particular task. A weakness of such approaches, however, is that they cannot deal well with humans who carry out the same task differently for different cases according to some hidden rule. The logs that are used for Task Mining generally do not contain sufficient data to distinguish the exact drivers behind this variability. In this paper, we propose a new Task Mining framework that has been designed to support engineers who wish to apply RPA to a task that is subject to variable human actions. This framework extracts features from User Interface (UI) Logs that are extended with a new source of data, namely screen captures. The framework invokes Supervised Machine Learning algorithms to generate decision models, which characterize the decisions behind variable human actions in a machine-and-human-readable form. We evaluated the proposed Task Mining framework with a set of synthetic UI Logs. Despite the use of only relatively small logs, our results demonstrate that a high accuracy is generally achieved.

Automated summary

Robotic Process Automation (RPA) is a way to automate repetitive tasks. Task Mining approaches can be used to discover human actions in carrying out tasks. However, existing approaches have difficulty dealing with humans who follow hidden rules to perform tasks differently. This paper proposes a new Task Mining framework that extracts features from UI logs and screen captures, and uses supervised machine learning algorithms to generate decision models for variable human actions.