By
Rina Dechter, University of California, Irvine
Description
Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively
focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of
constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial
intelligence, databases and programming languages, operations research, management science, and applied mathematics. Today, constraint
problems are used to model cognitive tasks in vision, language comprehension, default reasoning, diagnosis, scheduling, temporal and
spatial reasoning.
In Constraint Processing, Rina Dechter, synthesizes these contributions, along with her own significant work,
to provide the first comprehensive examination of the theory that underlies constraint processing algorithms. Throughout, she focuses
on fundamental tools and principles, emphasizing the representation and analysis of algorithms.
Audience:
graduate students and senior undergraduate students in artificial intelligence and operations research; researchers and practitioners in artificial intelligence and operations research
