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Type | Conference or Workshop Paper |
Scope | Discipline-based scholarship |
Published in Proceedings | Yes |
Title | Solving Distributed Constraint Optimization Problems Using Ranks |
Organization Unit | |
Authors |
|
Presentation Type | paper |
Item Subtype | Original Work |
Refereed | Yes |
Status | Published in final form |
Language |
|
ISBN | 978-1-57735-674-5 |
Page Range | 125 - 130 |
Event Title | Statistical Relational AI. Papers Presented at the Twenty-Eighth AAAI Conference on Artificial Intelligence. |
Event Type | workshop |
Event Location | Quebec City, Canada |
Event Start Date | July 27 - 2014 |
Event End Date | July 27 - 2014 |
Place of Publication | Palo Alto, California |
Publisher | AAAI Press |
Abstract Text | We present a variation of the classical Distributed Stochastic Algorithm (DSA), a local iterative best-response algorithm for Distributed Constraint Optimization Problems (DCOPs). We introduce weights for the agents, which influence their behaviour. We model DCOPs as graph processing problems, where the variables are represented as vertices and the constraints as edges. This enables us to create the Ranked DSA (RDSA), where the choice of the new state is influenced by the vertex rank as computed by a modified Page Rank algorithm. We experimentally show that this leads to a better speed of convergence to Nash Equilibria. Furthermore, we explore the trade-off space between average utility and convergence to Nash Equilibria, by using algorithms that switch between the DSA and RDSA strategies and by using heterogeneous graphs, with vertices using strategies in different proportions. |
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