On k-optimal distributed constraint optimization algorithms: new bounds and algorithms

Distributed constraint optimization (DCOP) is a promising approach to coordination, scheduling and task allocation in multi agent networks. In large-scale or low-bandwidth networks, finding the global optimum is often impractical. K-optimality is a promising new approach: for the first time it provides us a set of locally optimal algorithms with quality guarantees as a fraction of global optimum. Unfortunately, previous work in k-optimality did not address domains where we may have prior knowledge of reward structure; and it failed to provide quality guarantees or algorithms for domains with hard constraints (such as agents' local resource constraints). This paper addresses these shortcomings with three key contributions. It provides: (i) improved lower-bounds on k-optima quality incorporating available prior knowledge of reward structure; (ii) lower bounds on k-optima quality for problems with hard constraints; and (iii) k-optimal algorithms for solving DCOPs with hard constraints and detailed experimental results on large-scale networks.