Leveraging Multi-AI Agents for Cross-Domain Knowledge Discovery
Shiva Aryal Tuyen Do Bisesh Heyojoo Sandeep Chataut Bichar Dip Shrestha Gurung Venkataramana Gadhamshetty Etienne Gnimpieba
In the rapidly evolving field of artificial intelligence, the ability to
harness and integrate knowledge across various domains stands as a paramount
challenge and opportunity. This study introduces a novel approach to
cross-domain knowledge discovery through the deployment of multi-AI agents,
each specialized in distinct knowledge domains. These AI agents, designed to
function as domain-specific experts, collaborate in a unified framework to
synthesize and provide comprehensive insights that transcend the limitations of
single-domain expertise. By facilitating seamless interaction among these
agents, our platform aims to leverage the unique strengths and perspectives of
each, thereby enhancing the process of knowledge discovery and decision-making.
We present a comparative analysis of the different multi-agent workflow
scenarios evaluating their performance in terms of efficiency, accuracy, and
the breadth of knowledge integration. Through a series of experiments involving
complex, interdisciplinary queries, our findings demonstrate the superior
capability of domain specific multi-AI agent system in identifying and bridging
knowledge gaps. This research not only underscores the significance of
collaborative AI in driving innovation but also sets the stage for future
advancements in AI-driven, cross-disciplinary research and application. Our
methods were evaluated on a small pilot data and it showed a trend we expected,
if we increase the amount of data we custom train the agents, the trend is
expected to be more smooth.