SOCIAL DCOP - Social choice in distributed constraints optimization

Arnon Netzer, Amnon Meisels

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

7 Scopus citations

Abstract

Distributed Social Constraints Optimization Problems (DSCOPs) are DCOPs in which the global objective function for optimization incorporates a social welfare function (SWF). DSCOPs have individual, non-binary and asymmetric constraints and thus form a unique DCOP class. DSCOPs provide a natural framework for agents that compute their costs individually and are therefore self-interested. The concept of social welfare is discussed and SWFs are presented. An important aspect of DSCOPs and of social objective functions is their ability to support distributed hill climbing algorithms. The DSCOP hill climbing algorithm is presented and tested experimentally on multi agent pickup and delivery problems. It turns out to improve the distribution of utility gains among agents, while loosing very little in global gain.

Original languageEnglish
Title of host publicationIntelligent Distributed Computing V
Subtitle of host publicationProceedings of the 5th International Symposium on Intelligent Distributed Computing - IDC 2011, Delft, The Netherlands - October 2011
EditorsFrances M.T. Brazier, Kees Nieuwenhuis, Gregor Pavlin, Martijn Warnier, Costin Badica
Pages35-47
Number of pages13
DOIs
StatePublished - 2011
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume382
ISSN (Print)1860-949X

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