Complexity and Approximations in Robust Coalition Formation via Max-Min k-Partitioning,

Anisse Ismaili, Noam Hazon, Emi Watanabe, Makoto Yokoo, Sarit Kraus

Research output: Contribution to journalArticlepeer-review


© 2019 International Foundation for Autonomous Agents and Multiagent Systems ( All rights reserved. Coalition formation is beneficial to multi-agent systems, especially when the value of a coalition depends on the relationship among its members. However, an attack can significantly damage a coalition structure by disabling agents. Therefore, getting prepared in advance for such an attack is particularly important. We study a robust k-coalition formation problem modeled by max-min k-partition of a weighted graph. We show that this problem is Σp2-complete, which holds even for k = 2 and arbitrary weights, or k = 3 and non-negative weights. We also propose the Iterated Best Response (IBR) algorithm which provides a run-time absolute bound for the approximation error and can be generalized to the max-min optimization version of any Σp2-complete problem. We tested IBR on fairly large instances of both synthetic graphs and real life networks, yielding near optimal results in a reasonable time.
Original languageEnglish
Pages (from-to)2036-2038
Number of pages3
JournalProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
StatePublished - 1 Jan 2019


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