用谢泼德值公平分配不可分物品，如议会席位或图像关键区域。
Fair Indivisible Payoffs through Shapley Value
- 提出不可分谢泼德值，实现多玩家间公平分配
- 在图像分类中识别出影响结果的关键区域
- 适用于需公平分配实物的场景，如选举或医疗资源
我们研究不可分联盟博弈中的收益分配问题，其中全联合体的价值为自然数，代表不可分割的实体，如议会席位、肾脏交换或机器学习模型中对结果有贡献的顶级特征。本文提出一种基于不可分谢泼德值的公平分配方法，并分析其性质。通过三个案例研究验证该方法，尤其在图像分类任务中用于识别图像中对预测结果具有重要影响的区域。
原文摘要 · Abstract (English)
We consider the problem of payoff division in indivisible coalitional games, where the value of the grand coalition is a natural number. This number represents a certain quantity of indivisible objects, such as parliamentary seats, kidney exchanges, or top features contributing to the outcome of a machine learning model. The goal of this paper is to propose a fair method for dividing these objects among players. To achieve this, we define the indivisible Shapley value and study its properties. We demonstrate our proposed technique using three case studies, in particular, we use it to identify key regions of an image in the context of an image classification task.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。