Compute-Intensive Methods and Hybrid Approaches for Combinatorial Problems

摘要

Our research program focuses on techniques that lie at the intersection of Artificial Intelligence and Operations Research. In particular, we study computational methods for large-scale combinatorial optimization. Our research combines formal analysis and design of optimization techniques with the study of applications such as planning and scheduling, autonomous distributed agents and combinatorial auctions. Central themes of our work are (1) the integration of concepts from mathematical programming with constraint programming, (2) the study of the impact of structure on problem hardness, and (3) the use of randomization techniques to improve the performance of exact (complete) search methods. This report highlights some of our research projects and accomplishments.