We consider the problem of forming collectives of agents for real-world applications aligned with Sustainable Development Goals (e.g., shared mobility, cooperative learning). We propose a general approach for the formation of collectives based on a novel combination of an attention model and an integer linear program (ILP). In more detail, we propose an attention encoder-decoder model that transforms a collective formation instance to a weighted set packing problem, which is then solved by an ILP. Results on two real-world domains (i.e., ridesharing and team formation for cooperative learning) show that our approach provides solutions that are comparable (in terms of quality) to the ones produced by state-of-the-art approaches specific to each domain. Moreover, our solution outperforms the most recent general approach for forming collectives based on Monte Carlo tree search.

本研究提出了基于注意力模型和整数线性规划(ILP)的方法，通过将集体形成实例转化为加权集装问题进行求解，用于实现与可持续发展目标(例如共享出行、合作学习等)一致的应用程序中的代理形成集体的问题。研究结果表明，与特定于每个领域的最新现有方法以及最近的基于蒙特卡罗树搜索的通用方法相比，我们的方法在两个真实世界域下（即，拼车和合作学习的团队形成）提供的解决方案在质量上是可比的，而且性能更出色。

真实领域集体形成的注意力模型