No-Commitment Branch and Bound Search for Distributed Constraint Optimization

Abstract

We present a new polynomial-space algorithm for solving Distributed Constraint Optimization problems (DCOP). The algorithm, called NCBB, is branch and bound search with modifications for efficiency in a multiagent setting. Two main features of the algorithm are: (a) using different agents to search non-intersecting parts of a search space concurrently, and (b) communicating lower bounds on solution cost every time there is a possibility the bounds might change due to changed variable assignments. The first leads to a better utilization of computational resources of multiple participating agents, while the second provides for more efficient pruning of search space. Experimental results show that NCBB has significantly better performance than another polynomial-space algorithm, ADOPT, on random graph coloring problems. Under assumptions of cheap communication it also has compara ble performance with DPOP despite using only polynomial memory as opposed to exponential memory for DPOP.