@Article{modi04a,
  author =	 {Pragnesh Jay Modi and Wei-Min Shen and Milind Tambe
                  and Makoto Yokoo},
  title =	 {Adopt: Asynchronous Distributed Constraint
                  Optimization with Quality Guarantees},
  journal =	 {Artificial Intelligence},
  year =	 2005,
  volume = 	 {16},
  number = 	 {1--2},
  pages = 	 {149--180},
  doi = 	 {10.1016/j.artint.2004.09.003},
  abstract =	 {The Distributed Constraint Optimization Problem
                  (DCOP) is a promising approach for modeling
                  distributed reasoning tasks that arise in multiagent
                  systems. Unfortunately, existing methods for DCOP
                  are not able to provide theoretical guarantees on
                  global solution quality while allowing agents to
                  operate asynchronously. We show how this failure can
                  be remedied by allowing agents to make local
                  decisions based on conservative cost estimates
                  rather than relying on global certainty as previous
                  approaches have done. This novel approach results in
                  a polynomial-space algorithm for DCOP named Adopt
                  that is guaranteed to find the globally optimal
                  solution while allowing agents to execute
                  asynchronously and in parallel. Detailed
                  experimental results show that on benchmark problems
                  Adopt obtains speedups of several orders of
                  magnitude over other approaches. Adopt can also
                  perform bounded-error approximation--it has the
                  ability to quickly find approximate solutions and,
                  unlike heuristic search methods, still maintain a
                  theoretical guarantee on solution quality.},
  keywords =     {multiagent dcop},
  url =		 {http://jmvidal.cse.sc.edu/library/modi04a.pdf},
  googleid = 	 {cYcMGnIO1UoJ:scholar.google.com/},
  cluster = 	 {5392232012072126321}
}