Solving real-world decision making problems with constraint programming
Dr. Christian Drescher
Member of Artificial Intelligence Research group, Mercedes-Benz
Industry is increasingly looking at automating cognitive tasks, including decision making
in challenges from operations research like production sequencing, staff rostering, job scheduling, and
bin packing. When using computers to automate such combinatorial problems, it is often easier to declare
what a computer program must accomplish rather than describing how to accomplish it as a sequence of
instructions. Constraint Programming follows the declarative programming paradigm by allowing users to
state the properties of the feasible solutions for a set of decision variables, and draws on a wide range
of constraint solving techniques from Artificial intelligence to automatically determine solutions to any
instance of the stated problem. This workshop gives a brief introduction to Constraint Programming using
examples that are accessible to a general audience, like solving Sudoku puzzles. Part of the session
allows for hands-on activity using the open source Constraint Programming toolkit MiniZinc. Participants
are encouraged to have the MiniZinc IDE installed on their laptop if they wish to experience Constraint
Programming in practice. However, it is not required to follow the contents of the workshop.
MiniZinc IDE