Advertisement

Connecting BnB-ADOPT with Soft Arc Consistency: Initial Results

  • Patricia Gutierrez
  • Pedro Meseguer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6384)

Abstract

Distributed constraint optimization problems with finite domains can be solved by asynchronous procedures. ADOPT is the reference algorithm for this kind of problems. Several versions of this algorithm have been proposed, one of them is BnB-ADOPT which changes the nature of the original algorithm from best-first to depth-first search. With BnB-ADOPT, we can assure in some cases that the value of a variable will not be used in the optimal solution. Then, this value can be deleted unconditionally. The contribution of this work consists in propagating these unconditionally deleted values using soft arc consistency techniques, in such a way that they can be known by other variables that share cost functions. When we propagate these unconditional deletions we may generate some new deletions that will also be propagated. The global effect is that we search in a smaller space, causing performance improvements. The effect of the propagation is evaluated on several benchmarks.

Keywords

Cost Function Unary Constraint Execution Trace Binary Constraint Constraint Optimization Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dechter, R.: Constraint Processing. Morgan Kaufmann, San Francisco (2003)zbMATHGoogle Scholar
  2. 2.
    Larrosa, J.: Node and arc consistency in weighted CSP. In: Proc. of AAAI 2002 (2002)Google Scholar
  3. 3.
    Larrosa, J., Schiex, T.: In the quest of the best form of local consistency for weighted CSP. In: Proc. of IJCAI 2003 (2003)Google Scholar
  4. 4.
    Meisels, A., Kaplansky, E., Razgon, I., Zivan, R.: Comparing performance of distributed constraint processing algorithms. In: AAMAS Workshop on Distributed Constraint Reasoning, pp. 86–93 (2002)Google Scholar
  5. 5.
    Meseguer, P., Rossi, F., Schiex, T.: Handbook of Constraint Programming. In: Soft Constraints, ch. 9. Elsevier, Amsterdam (2006)CrossRefGoogle Scholar
  6. 6.
    Modi, P.J., Shen, W.M., Tambe, M., Yokoo, M.: Adopt: asynchronous distributed constraint optimization with quality guarantees. Artificial Intelligence (161), 149–180 (2005)Google Scholar
  7. 7.
    Silaghi, M., Yokoo, M.: Nogood-based asynchronous distributed optimization (ADOPT-ng). In: Proc. of AAMAS 2006 (2006)Google Scholar
  8. 8.
    Yeoh, W., Felner, A., Koenig, S.: Bnb-adopt: An asynchronous branch-and-bound DCOP algorithm. In: Proc. of AAMAS 2008, pp. 591–598 (2008)Google Scholar
  9. 9.
    Yin, Z.: USC dcop repository. Meeting scheduling and sensor net datasets (2008), http://teamcore.usc.edu/dcop

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Patricia Gutierrez
    • 1
  • Pedro Meseguer
    • 1
  1. 1.IIIA, Institut d’Investigació en Intel.ligència Artificial, CSIC, Consejo Superior de Investigaciones Científicas, Campus UABBellaterraSpain

Personalised recommendations