  This study addresses the challenge of forming effective groups in
collaborative problem-solving environments. Recognizing the complexity of human
interactions and the necessity for efficient collaboration, we propose a novel
approach leveraging graph theory and reinforcement learning. Our methodology
involves constructing a graph from a dataset where nodes represent
participants, and edges signify the interactions between them. We conceptualize
each participant as an agent within a reinforcement learning framework, aiming
to learn an optimal graph structure that reflects effective group dynamics.
Clustering techniques are employed to delineate clear group structures based on
the learned graph. Our approach provides theoretical solutions based on
evaluation metrics and graph measurements, offering insights into potential
improvements in group effectiveness and reductions in conflict incidences. This
research contributes to the fields of collaborative work and educational
psychology by presenting a data-driven, analytical approach to group formation.
It has practical implications for organizational team building, classroom
settings, and any collaborative scenario where group dynamics are crucial. The
study opens new avenues for exploring the application of graph theory and
reinforcement learning in social and behavioral sciences, highlighting the
potential for empirical validation in future work.
