Towards Improving the Expressivity and Scalability of Distributed Constraint Optimization Problems

William Yeoh

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

Early Career. Pages 5734-5738.

https://doi.org/10.24963/ijcai.2018/819

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Abstract

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.

Keywords

  • Constraints and SAT: Distributed Constraints
  • Agent-based and Multi-agent Systems: Coordination and Cooperation
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