Harnessing Generative Adversarial Networks (GANs) for Advanced Robotic Process Automation and Intelligent Bot Development

This thesis investigates the synergies between Generative Adversarial Networks (GANs) and Intelligent Bot Development as well as Robotic Process Automation (RPA). Goals are to explore how GANs can boost RPA systems’ functionality through crude high fidelity synthetic data capabilities, improved natural language understanding which allows bots to handle complex user conversations. At the expected outcome the aim was to develop smarter and more flexible bots who can solve a wider range of problems in more flexible environment. Incorporating GANs, this research hopes to push forward RPA systems leading to more efficient, flexible, and intelligent automation solutions. In addition, the study will show how GANs enable organizations to rapidly prototype and simulate bot performance, and when combined with AI driven processes, make it easier to deploy new approaches that both improve bot performance, and human bot interaction. In the end, this work would reveal a full understanding of how GANs can dramatically transform intelligent automation by creating context understanding interactive and scalable bot systems.