@inproceedings{3d3c74d6594c4221af7305ea47ca5849,
title = "Blame Attribution for Multi-Agent Path Finding Execution Failures",
abstract = "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.",
author = "Avraham Natan and Roni Stern and Meir Kalech",
note = "Publisher Copyright: {\textcopyright} 2023 The Authors.; 26th European Conference on Artificial Intelligence, ECAI 2023 ; Conference date: 30-09-2023 Through 04-10-2023",
year = "2023",
month = sep,
day = "28",
doi = "10.3233/FAIA230462",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "1763--1770",
editor = "Kobi Gal and Kobi Gal and Ann Nowe and Nalepa, {Grzegorz J.} and Roy Fairstein and Roxana Radulescu",
booktitle = "ECAI 2023 - 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings",
address = "Netherlands",
}