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Résultat majeur : DISTRIBUTED TREE DECOMPOSITION WITH PRIVACY
DISTRIBUTED TREE DECOMPOSITION WITH PRIVACY
29 juin 2012

Vincent Armant, Laurent Simon and Philippe Dague in 18th International Conference on Principles and Practice of Constraint Programming
Tree Decomposition of Graphical Models is a well known method for mapping a graph into a tree, that is commonly used to speed up solving many problems in Graph theory, Constraints optimization, Planning, Databases, Knowledge Representation, ...
However, in a distributed case, one may have to respect the privacy rules (a subset of variables may have to be kept secret in a peer), and the initial network architecture (no link can be dynamically added). In this context, we propose a new distributed method, based on token passing and local elections, that shows performances (in the jointree quality) close to the state of the art Bucket Elimination in a centralized case ie when used without these two restrictions). Until now, the state of the art in distributed context was using a Depth-First traversal with a clever heuristic. It is outperformed by our method on two families of problems sharing the small-world property.



Activités de recherche
  ° Compilation de bases de connaissances
  ° Algorithmique répartie
  ° Optimisation
  ° Algorithmique de graphes
  ° Pair à Pair

Equipe
  ° Intelligence Artificielle et Systèmes d'Inférence

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