The Shapley value is arguably the most popular approach for assigning a meaningful contribution value to players in a cooperative game, which has recently been used intensively in explainable artificial intelligence. The meaningfulness is due to axiomatic properties that only the Shapley value satisfies, which, however, comes at the expense of an exact computation growing exponentially with the number of agents. Accordingly, a number of works are devoted to the efficient approximation of the Shapley values, most of them revolve around the notion of an agent's marginal contribution. In this paper, we propose with SVARM and Stratified SVARM two parameter-free and domain-independent approximation algorithms based on a representation of the Shapley value detached from the notion of marginal contributions. We prove unmatched theoretical guarantees regarding their approximation quality and provide empirical results including synthetic games as well as common explainability use cases comparing ourselves with state-of-the-art methods.

翻译：Shapley值可被视为合作博弈中为参与者分配有意义的贡献值时最流行的方法，近年来在可解释人工智能领域得到了广泛应用。其意义源于Shapley值唯一满足的公理化性质，但这以精确计算复杂度随智能体数量指数增长为代价。因此，众多研究致力于高效近似Shapley值，其中多数方法围绕参与者的边际贡献概念展开。本文提出SVARM和分层SVARM两种无参数、领域无关的近似算法，其基础是脱离边际贡献概念的Shapley值表示形式。我们证明了这些算法在近似质量上具有无与伦比的理论保证，并通过包含合成博弈及常见可解释性用例的实证研究，与现有最优方法进行了对比。