Multiagent Constraint Satisfaction with Multiply Sectioned Constraint Networks

Variables and constraints in problem domains are often distributed. These distributed constraint satisfaction problems (DCSPs) lend themselves to multiagent solutions. Most existing algorithms for DCSPs are extensions of centralized backtracking or iterative improvement with breakout. Their worst case complexity is exponential. On the other hand, directional consistency based algorithms solve centralized CSPs efficiently if primal graph density is bounded. No known multiagent algorithms solve DCSPs with the same efficiency. We propose the first such algorithm and show that it is sound and complete.