Towards Improving the Expressivity and Scalability of Distributed Constraint Optimization Problems

Constraints have long been studied in centralized systems and have proven to be practical and efficient for modeling and solving resource allocation and scheduling problems. Slightly more than a decade ago, researchers proposed the distributed constraint optimization problem (DCOP) formulation, which is well suited for modeling distributed multi-agent coordination problems. In this paper, we highlight some of our recent contributions that are aiming towards improved expressivity of the DCOP model as well as improved scalability of the accompanying algorithms.