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

Abstract

© 2019 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). 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
Volume4
StatePublished - 1 Jan 2019

Funding

This work was partially supported by the Ministry of Science and Technology, Israel, the Japan Science and Technology Agency (JST) Strategic International Collaborative Research Program, and JSPS KAKENHI Grant Number JP17H00761.

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