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.

翻译：本研究针对协作问题解决环境中有效分组的挑战，提出了一种融合图理论与强化学习的新型方法。鉴于人类互动的复杂性及高效协作的必要性，我们从数据集中构建图结构——节点代表参与者，边表示其交互关系。在强化学习框架下，将每位参与者视为智能体，旨在学习能反映有效群体动态的最优图结构。基于学习到的图结构，采用聚类技术划分清晰的群组结构。本方法基于评估指标与图测量指标提供理论解决方案，揭示了提升群体效能、减少冲突次数的潜在改进方向。该研究通过提出数据驱动的分析型分组方法，为协作工作与教育心理学领域做出贡献，对组织团队建设、课堂环境及任何依赖群体动态的协作场景具有实际应用价值。研究开创了图理论与强化学习在社会行为科学领域应用的新路径，并凸显了未来实证验证的潜力。