论文标题
现实世界中集体形成的注意力模型
An attention model for the formation of collectives in real-world domains
论文作者
论文摘要
我们考虑组成与可持续发展目标（例如共享流动性，合作学习）一致的现实应用程序集体集体的问题。我们提出了一种基于注意力模型和整数线性程序（ILP）的新组合形成集体的一般方法。更详细地，我们提出了一个注意编码器模型，该模型将集体形成实例转换为加权设定的填料问题，然后由ILP解决。结果在两个现实世界中的域（即合作学习的乘车共享和团队形成）表明，我们的方法提供了与特定于每个领域的最先进方法相比的解决方案（在质量方面）。此外，我们的解决方案优于基于蒙特卡洛树搜索组成集体的最新一般方法。
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.
