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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Cuilt: a Scalable, Mix-and-Match Framework for Local Iterative Approximate Best-Response Algorithms
Organization Unit
  • Contribution from another University/Organization than University of Zurich
  • Coralia-Mihaela Verman
  • Philip Stutz
  • Robin Hafen
  • Abraham Bernstein
  • Gal A Kaminka
  • Maria Fox
  • Paolo Bouquet
  • Eyke Hüllermeier
  • Virginia Dignum
  • Frank Dignum
  • Frank van Harmelen
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
ISBN 978-1-61499-671-2 (print) | 978-1-61499-672-9 (online)
ISSN 0922-6389
Page Range 1660 - 1661
Event Title 22nd European Conference on Artificial Intelligence
Event Type conference
Event Location The Hague, The Netherlands
Event Start Date August 29 - 2016
Event End Date September 2 - 2016
Series Name Frontiers in Artificial Intelligence and Applications
Number 285
Publisher I O S Press
Abstract Text Many real-world tasks can be modeled as constraint optimization problems. To ensure scalability and mapping to distributed scenarios, distributed constraint optimization problems (DCOPs) have been proposed, where each variable is locally controlled by its own agent. Most practical applications prefer approximate local iterative algorithms to reach a locally optimal and sufficiently good solution fast. Most implementations presented in the literature, however, only explored small-sized problems, typically up to 100 agents/variables. We implement CUILT, a scalable mix-and-match framework for Local Iterative Approximate Best-Response Algorithms for DCOPs, using the graph processing framework SIGNAL/COLLECT, where each agent is modeled as a vertex and communication pathways are represented as edges. Choosing this abstraction allows us to exploit the generic graph-oriented distribution/optimization heuristics and makes our proposed framework scalable, configurable, as well as extensible. We found that this approach allows us to scale to problems more than 3 orders of magnitude larger than results commonly published so far, to easily combine algorithms by mixing and matching, and to run the algorithms fast, in a parallel fashion.
Free access at DOI
Official URL
Digital Object Identifier 10.3233/978-1-61499-672-9-1660
PubMed ID
Other Identification Number merlin-id:13478
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