AUTONODE: A Neuro-Graphic Self-Learnable Engine for Cognitive GUI
  Automation
Arkajit Datta Tushar Verma Rajat Chawla
In recent advancements within the domain of Large Language Models (LLMs),
there has been a notable emergence of agents capable of addressing Robotic
Process Automation (RPA) challenges through enhanced cognitive capabilities and
sophisticated reasoning. This development heralds a new era of scalability and
human-like adaptability in goal attainment. In this context, we introduce
AUTONODE (Autonomous User-interface Transformation through Online Neuro-graphic
Operations and Deep Exploration). AUTONODE employs advanced neuro-graphical
techniques to facilitate autonomous navigation and task execution on web
interfaces, thereby obviating the necessity for predefined scripts or manual
intervention. Our engine empowers agents to comprehend and implement complex
workflows, adapting to dynamic web environments with unparalleled efficiency.
Our methodology synergizes cognitive functionalities with robotic automation,
endowing AUTONODE with the ability to learn from experience. We have integrated
an exploratory module, DoRA (Discovery and mapping Operation for graph
Retrieval Agent), which is instrumental in constructing a knowledge graph that
the engine utilizes to optimize its actions and achieve objectives with minimal
supervision. The versatility and efficacy of AUTONODE are demonstrated through
a series of experiments, highlighting its proficiency in managing a diverse
array of web-based tasks, ranging from data extraction to transaction
processing.