In Multi-Agent Systems (MAS), Multi-Agent Path Finding (MAPF) is the problem of finding a conflict-free plan for a group of agents from a set of starting points to a set of target points. Deviations from this plan are standard in real-world applications and may decrease overall system efficiency and even lead to accidents and deadlocks. In large MAS scenarios with physical robots, multiple faulty events occur over time, contributing to the overall degraded system performance. This raises the main problem we address in this work: how to attribute blame for a degraded MAS performance over a set of faulty events. We formally define this problem and propose using the Shapley values to solve it. Then, we propose an algorithm that efficiently approximates Shapley values by considering only some subsets of faulty events set. We analyze this algorithm theoretically and experimentally and demonstrate that it enables effectively trading off runtime for error.
|Title of host publication
|ECAI 2023 - 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings
|Kobi Gal, Kobi Gal, Ann Nowe, Grzegorz J. Nalepa, Roy Fairstein, Roxana Radulescu
|IOS Press BV
|Number of pages
|Published - 28 Sep 2023
|26th European Conference on Artificial Intelligence, ECAI 2023 - Krakow, Poland
Duration: 30 Sep 2023 → 4 Oct 2023
|Frontiers in Artificial Intelligence and Applications
|26th European Conference on Artificial Intelligence, ECAI 2023
|30/09/23 → 4/10/23
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© 2023 The Authors.