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：沙普利值可以说是为合作博弈中参与者分配有意义贡献值的最流行方法，近年来在可解释人工智能领域被广泛使用。其意义性源于只有沙普利值满足的公理化性质，然而，这以精确计算量随智能体数量呈指数增长为代价。因此，大量研究致力于高效近似沙普利值，其中大部分围绕智能体边际贡献的概念展开。本文基于脱离边际贡献概念的沙普利值表示，提出了两种无参数且与领域无关的近似算法SVARM和分层SVARM。我们证明了它们在逼近质量上无与伦比的理论保证，并提供了包括合成博弈和常见可解释性用例在内的实证结果，与最先进方法进行了比较。