Distributed Constraint Optimization

Improving DPOP with Branch Consistency for Solving Distributed Constraint Optimization Problems

Ferdinando Fioretto, Tiep Le, William Yeoh, Enrico Pontelli, Tran Cao Son.
In Proceedings of the International Conference on Principles and Practice of Constraint Programming. (to appear), 2014.


The DCOP model has gained momentum in recent years thanks to its ability to capture problems that are naturally distributed and cannot be realistically addressed in a centralized manner. Dynamic programming based techniques have been recognized to be among the most effective techniques for building complete DCOP solvers (e.g., DPOP). Unfortunately, they also suffer from a widely recognized drawback: their messages are exponential in size. Another limitation is that most current DCOP algorithms do not actively exploit hard constraints, which are common in many real problems. This paper addresses these two limitations by introducing an algorithm, called BrC-DPOP, that exploits arc consistency and a form of consistency that applies to paths in pseudo-trees to reduce the size of the messages. Experimental results shows that BrC-DPOP uses messages that are up to one order of magnitude smaller than DPOP, and that it can scale up well, being able to solve problems that its counterpart can not.


A copy of the paper, the bibtex file, and the experiments, executables and results linked with the paper are available for download.