Invited Speakers

Unlike mathematical programming and SAT solving, constraint programming (CP) is based on the idea that both modeling and solving of combinatorial optimization problems can be based on conjunctions of loosely coupled, recurring, combinatorial subproblems (also known as "global constraints"). This rich representational approach means that, for better or for worse, pretty much anything can be expressed as a global constraint. Much of CP's success, however, has come from exploiting only one aspect of the rich constraint definition: global constraint propagation. In this talk, I will investigate how work in CP, SAT, AI planning, and mathematical programming can be understood as more seriously pursuing the implications of a rich constraint definition and how the interplay between local and global information can lead to dynamic problem reformulations and a flexible hybrid solver architecture. J. Christopher Beck is an associate professor and associate chair, research in the Department of Mechanical and Industrial Engineering, University of Toronto. Beck's MSc and PhD degrees both come from the Department of Computer Science, University of Toronto, in the area of artificial intelligence. Beck spent three years at ILOG as a senior scientist and software engineer on the ILOG Scheduler team before spending two years as a staff scientist at the Cork Constraint Computation Centre. He returned to Toronto in 2004 to join the Department of Mechanical and Industrial Engineering. Beck's research interests include scheduling, constraint programming, AI planning, reasoning under uncertainty, queueing theory, mixed integer programming, and hybrid optimization techniques. Beck currently serves in an editorial capacity for four journals and one website in AI and OR. He is the president-elect of the Executive Council for the International Conference on Automated Planning and Scheduling. Applications of Graph Search in Group Theory and Proteomics