Hi, I'm Akshay, a Machine Learning researcher and Software Engineer from Massachusetts.

Accurately predicting the states of multi-agent systems and identifying their

underlying graph structures remains a difficult challenge. Our work introduces a novel machine learning-based solution that leverages the attention mechanism to predict future states of multi- agent systems by learning node representations. The graph structure is then inferred from the strength of the attention values. This approach is applied to both linear consensus dynamics and the non-linear dynamics of Kuramoto oscillators, learning the graph by learning good agent representations. Our results demonstrate that the presented data-driven graph attention machine learning model can identify the network topology in multi-agent systems with unknown dynamics, as evidenced by the F1 scores achieved in the link prediction.

In the Proceedings of the MECC 2024 conference