Annnouncements:

Each semester we host one to two visitors. This semester's visitors are still to be determined. Check the list of previous visitors.

Class schedule: the page will be regularly updated. Check it out regularly for announcements of required/recommended reading material, homework, etc.

General Information

Prereq: Instructor's permission

Course description: Constraint satisfaction has emerged as a powerful approach to articulate and solve many problems in computer science, engineering, and management.  It is now the basis for new programming languages and innovative commercial systems for production scheduling, product configuration, personnel planning and timetabling, etc.  The goal of this course is to prepare students to conduct research in this area. The course will be intensive and will require thorough study of the theory and the algorithms, and a significant implementation effort. Students are expected to be self motivated, and demonstrate intellectual independence and collegial collaboration.

Lectures: Monday, Wednesday 8:00 a.m. to 9:15 a.m.
Location: Burnett 120.

Make-up Class/Recitation: By agreement
Location: TBA.

Instructor:   Prof.  Berthe Y. Choueiry
      Office location: Room 360, Avery Hall,
      choueiry AT cse.unl.edu, tel: (402)472-5444.
      Office hours: Monday/Wednesday 9:30--10:30 a.m. and 6:00--7:00 p.m. or by appointment.

Volunteer GTA's & Office Hours:

Textbook:
"Constraint Processing" by Rina Dechter, 1^st edition. The textbook will not be followed sequentially, but should be used for reference.

For quick response, email cse496cp AT cse.unl.edu.  Your message will be forwarded to TA(s) and instructor.

Important note:We do not have a GTA allocated to this class. The two GTA's are volunteers. Please send all questions to cse496cp AT cse.unl.edu for a quick response.

Topics include but are not restricted to:

• Properties, computational complexity, and practical importance.
• Global and local consistency: algorithms, properties and computational complexity.
• Islands of tractability for minimality and global consistency.
• Intelligent backtracking.
• Look-ahead techniques.
• Ordering heuristics.
• Theoretical and empirical comparison of hybrid search algorithms.
• Phase transition.
• Modeling and reduction methods between representations.
• If time permits: Decomposition. Symmetries and their approximations. Temporal constraint networks. Stochastic search. Reformulation and abstraction. Dynamic/conditional Constraint Satisfaction (CSP). Constrained Optimization Problem (COP).
• Other, depending on class interests.

Support:

• Rina Dechter, Constraint Processing, 2003, Morgan Kauffmann.  Available from bookstore.
• Lecoutre's e-textbook on Constraint Networks (available from UNL's libaries)
• Edward Tsang, Foundations of Constraint Satisfaction. Also, available from instructor and on reserve at the Math Library in Avery Hall.
• Technical papers given by instructor or available from the Electronic Reserve at the Love Library.

Last modified: Mon Oct 3 13:20:02 CDT 2011