Using Graph Properties and Clustering Techniques to Select Division Mechanisms for Scalable Negotiations

Ivan Marsa Maestre, Catholijn Jonker, Mark Klein, Enrique de la Hoz

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

Abstract

This paper focuses on enabling the use of negotiation for complex system optimisation, which main challenge nowadays is scalability. Our hypothesis is that analysing the underlying network structure of these systems can help divide the problems in subproblems which facilitate distributed decision making through negotiation in these domains. In this paper, we verify this hypothesis with an extensive set of scenarios for a proof-of-concept problem. After selecting a set of network metrics for analysis, we cluster the scenarios according to these metrics and evaluate a set of mediation mechanisms in each cluster. The validation experiments show that the relative performance of the different mediation mechanisms change for each cluster, which confirms that network-based metrics may be useful for mechanism selection in complex networks.
Original languageEnglish
Title of host publicationModern Approaches to Agent-based Complex Automated Negotiation
EditorsKatsuhide Fujita, Quan Bai, Takayuki Ito, Minjie Zhang, Fenghui Ren, Reyhan Aydoğan, Rafik Hadfi
Place of PublicationCham
PublisherSpringer Science+Business Media
Pages67-84
Number of pages18
Volume674
Edition1
ISBN (Electronic)978-3-319-51563-2
ISBN (Print)978-3-319-51561-8
DOIs
Publication statusPublished - 9 Apr 2017

Publication series

NameStudies in Computational Intelligence
Volume674
ISSN (Print)1860-949X

Fingerprint

Dive into the research topics of 'Using Graph Properties and Clustering Techniques to Select Division Mechanisms for Scalable Negotiations'. Together they form a unique fingerprint.

Cite this