Dr. Rina Dechter - University of California at Irvine ZOT!
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CompSci 275 Winter 2016, Constraint Networks
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  • Instructor: Rina Dechter
  • Section: 34985
  • Classoom: DBH 1423
  • Days: Tuesday & Thursday
  • Time: 12:30 pm - 1:50 pm
  • Office hours: Tuesday 3:00 pm - 4:00 pm
  • Exam: Mar 3rd, in class

Course Goals
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.

The purpose of this course is to familiarize students with the theory and techniques of constraint processing, using the constraint graphical model. This model offers a natural language for encoding world knowledge in areas such as scheduling, vision, diagnosis, prediction, design, hardware and software verification, and bio-informatics, and it facilitates many computational tasks relevant to these domains such as constraint satisfaction, constraint optimization, counting and sampling . The course will focus on techniques for constraint processing. It will cover search and inference algorithms, consistency algorithms and structure based techniques and will focus on properties that facilitate efficient solutions. Extensions to general graphical models such as probabilistic networks, cost networks, and influence diagrams will be discussed as well as example applications such as temporal reasoning, diagnosis, scheduling, and prediction.


Required textbook: Rina Dechter, Constraint Processing, Morgan Kaufmann

Grading Policy
Homeworks and projects (80%), midterm (20%).

There will be weekly homework-assignments, a project, and an exam.


Project Information

Week Topic Slides
Additional Reading
Week 1
  • Chapters 1,2: Introductions to constraint network model. Graph representations, binary constraint networks.
Set 1

Numberjack Tutorial
Homework 1
(due 01-14)

Code Examples

Week 2
  • Chapter 3: Constraint propagation and consistency enforcing algorithms, arc, path and i-consistency
Set 2

Lecture 3

Lecture 4
Homework 2
(due 01-21)
Constraint Propragation by Christian Bessiere 01-12


Week 3
  • Chapter 4: Graph concepts (induced-width), Directional consistency, Adaptive-consistency, bucket-elimination.
Set 3 Lecture 5

Lecture 6
Homework 3
(due 01-28)
The Sat Solving Revolution: Solving, Sampling and Counting by Moshe Vardi 01-19


Week 4
  • Chapter 5: Backtracking search: Look-ahead schemes: forward-checking, variable and value orderings. DPLL.
Set 4 Lecture 7

Lecture 8
Homework 4
(due 02-04)
Complete Algorithms by Darwiche and Pipatsrisawat 01-26

Week 5
  • Chapter 6: Backtracking search; Look-back schemes: backjumping, constraint learning. SAT solving and solvers (e.g., MAC, Minisat).
Set 5 Lecture 9

Lecture 10
Homework 5
(due 02-11)

Week 6
  • Chapter 7: Stochastic local search, SLS, GSAT, WSAT
  • Satisfiability solving

Set 6
Lecture 11

Lecture 12
SATHandbook-CDCL 02-09

Week 7
  • Chapter 8: Advanced consistency methods; relational consistency and bucket-elimination, row-convexity, tightness, looseness.
Set 7

Set 8
Lecture 13

Lecture 14
Homework 6
AND/OR Search Spaces for Graphical Models 02-16

Week 8
  • Chapter 13: Constraint Optimization, soft constraints
Set 9 Lecture 15

Lecture 16
Homework 7

Week 9
  • Chapter 13: Constraint Optimization, soft constraints (continued)

Exam, in class

Week 10

    Week 11

      Resources on the Internet