@InProceedings{petcu05a,
  author =	 {Adrian Petcu and Boi Faltings},
  title =	 {A Scalable Method for Multiagent Constraint
                  Optimization},
  booktitle =	 {Proceedings of the International Joint Conference on
                  Artificial Intelligence},
  year =	 {2005},
  address =	 {Edinburgh, Scotland},
  month =	 aug,
  pages =	 {266--271},
  abstract =	 {We present in this paper a new, complete method for
                  distributed constraint optimization, based on
                  dynamic programming. It is a utility propagation
                  method, inspired by the sum-product algorithm, which
                  is correct only for tree-shaped constraint
                  networks. In this paper, we show how to extend that
                  algorithm to arbitrary topologies using a pseudotree
                  arrangement of the problem graph. Our algorithm
                  requires a linear number of messages, whose maximal
                  size depends on the induced width along the
                  particular pseudotree chosen. We compare our
                  algorithm with backtracking algorithms, and present
                  experimental results. For some problem types we
                  report orders of magnitude less messages, and even
                  the ability to deal with arbitrarily large
                  problems. Our algorithm is formulated for
                  optimization problems, but can be easily applied to
                  satisfaction problems as well.},
  url = 	 {http://jmvidal.cse.sc.edu/library/petcu05a.pdf},
  cluster = 	 {15369336249688638382},
  keywords = 	 {multiagent dcop}
}