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Kolpaczki, Patrick; Bengs, Viktor ORCID logoORCID: https://orcid.org/0000-0001-6988-6186; Muschalik, Maximilian ORCID logoORCID: https://orcid.org/0000-0002-6921-0204 and Hüllermeier, Eyke ORCID logoORCID: https://orcid.org/0000-0002-9944-4108 (2024): Approximating the Shapley Value without Marginal Contributions. AAAI Conference on Artificial Intelligence 2024, Vancouver, Canada, 20-27 February 2024. Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38, No. 12 pp. 13246-13255

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The Shapley value, which is arguably the most popular approach for assigning a meaningful contribution value to players in a cooperative game, has recently been used intensively in explainable artificial intelligence. Its 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 value, 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 contribution. 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.

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