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CompSci 275, Constraint Networks, Fall 2010
home work | slides | readings | project

  • Instructor: Rina Dechter
  • Section: 35340
  • Classoom: ICS 253
  • Days: Tuesday & Thursday
  • Time: 11:00 - 12:20 pm
  • Office hours: Tuesday & Thursday 1:00-2:00 pm, DBH 4232


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.


Textbook

Required textbook: Rina Dechter, Constraint Processing, Morgan Kaufmann

Additional readings


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


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


Syllabus:
Subject to changes

Week Topic Slides
Homework
Date  
Week 0
  • Chapters 1,2: Introductions to constraint network model. Graph representations, binary constraint networks.
Class 1 

                    
09-23
Week 1
  • Chapter 3: Constraint propagation and consistency enforcing algorithms, arc, path and i-consistency
Chapter 3 
Homework 1  
 
Solutions 1
09-28

09-30
Week 2
  • Chapter 4: Graph concepts (induced-width), Directional consistency, Adaptive-consistency, bucket-elimination.
Chapter 4 
Homework 2, due Oct 12  
 
Solutions 2
10-05

10-07

Week 3
  • Chapter 4 (continued)
 
Homework 3, due Oct 19 
 
Homework 3 solutions
10-12

10-14
Week 4
  • Chapter 5: Backtracking search: Look-ahead schemes: forward-checking, variable and value orderings. DPLL.
Chapter 5 
 
Homework 4, due Oct 26 
 
Homework 4 solutions

10-19

10-21
Week 5
  • Chapter 6: Backtracking search; Look-back schemes: backjumping, constraint learning. SAT solving and solvers (e.g., MAC, Minisat).
Chapter 6  Homework 5, due Nov 4  (Due: 04/11)
Minisat
WALKSAT
RSAT

 
10-26

10-27
Week 6
  • Chapter 7: Stochastic local search, SLS, GSAT, WSAT
  • Satisfiability solving
Satisfiability    11-02

11-04
Week 7
  • Chapter 8: Advanced consistency methods; relational consistency and bucket-elimination, row-convexity, tightness, looseness.
  • No class Thursday 11/11 - Veteran's day!
Chapter 7 Chapter 8 Homework 6, due Nov 16  11-09

11-11
Week 8
  • Chapter 9: Tree Clustering, treewidth and hypertree width
Chapter 9
Homework 7, due Nov 30 11-16

11-18
Week 9
  • Chapter 13: Constraint Optimization, soft constraints
  • No class 11/25 - Thanksgiving!
Chapter 13

11-23

11-25
Week 10
  • Project presentations (9:30-12:30)
  • Midterm


11-30

12-02
Week 11
  • Finals week: project presentations (10:00-4:00, DBH 4011)


12-07

12-09

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