Automated Robotic Processes that Learn on Their Own

Recent years have seen a substantial increase in interest in robotic process automation (RPA), both in business and academics. RPA is a group of technologies and processes that provide company owners the ability to automate routine manual operations. RPA has unquestionable inherent value since automation reduces mistakes and expenses while increasing the efficiency of all business processes. However, the use of modern RPA systems requires human work for locating, explaining, and programming activities intended for automation. Various methods exist for precisely monitoring users' front-end behavior concurrently. In this work, we provide a ground-breaking end-to-end methodology that enables algorithmic RPA rule derivation that is totally automated and based on observed user behaviour. We also provide a freely available proof-of-concept version of our suggested strategy.