In many multi-agent settings, participants can form teams to achieve collective outcomes that may far surpass their individual capabilities. Measuring the relative contributions of agents and allocating them shares of the reward that promote long-lasting cooperation are difficult tasks. Cooperative game theory offers solution concepts identifying distribution schemes, such as the Shapley value, that fairly reflect the contribution of individuals to the performance of the team or the Core, which reduces the incentive of agents to abandon their team. Applications of such methods include identifying influential features and sharing the costs of joint ventures or team formation. Unfortunately, using these solutions requires tackling a computational barrier as they are hard to compute, even in restricted settings. In this work, we show how cooperative game-theoretic solutions can be distilled into a learned model by training neural networks to propose fair and stable payoff allocations. We show that our approach creates models that can generalize to games far from the training distribution and can predict solutions for more players than observed during training. An important application of our framework is Explainable AI: our approach can be used to speed-up Shapley value computations on many instances.

翻译：在许多多试剂环境下,参与者可以组建团队,以取得可能远远超过其个人能力的集体成果。衡量代理人的相对贡献并分配他们分享促进长期合作的奖励份额是困难的任务。合作游戏理论提供了确定分配办法的解决方案概念,例如Shapley价值,这种分配办法能够公平反映个人对团队或核心业绩的贡献,从而减少代理人放弃团队的动力。这些方法的应用包括确定有影响力的特点并分担合资企业或团队组建的费用。不幸的是,使用这些解决方案需要解决计算障碍,因为它们难以计算,即使在受限制的环境中也是如此。在这项工作中,我们展示如何通过培训神经网络将合作的游戏理论解决方案提炼成一个学习的模型,以提出公平和稳定的薪酬分配。我们表明,我们的方法创造了模型,这些模型可以比培训分配的更远地概括游戏,并且可以预测比培训期间观察到的更多参与者的解决方案。我们框架的一个重要应用是解释性AI:我们的方法可以用来加快许多实例的沙皮值计算。