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CompSci 275, Constraint Networks, Spring 2009
home work | slides | readings | project

  • Instructor: Rina Dechter
  • Section: 35340
  • Classoom: DBH 1425
  • Days: Monday & Wednesday
  • Time: 11:00 - 12:20 pm


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 (75%), Exam (25%).


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


Syllabus:
Subject to changes

Week Topic Slides
Homework
Date  
Week 1
  • Chapters 1,2: Introductions to constraint network model, and general graphical models. Graph representations, properties of binary constraint networks.
Chap.1 and 2
Emma's slides
                    
03-30

04-01
Week 2
  • Chapter 3: Consistency enforcing algorithms, arc, path and i-consistency
Chap. 3
Homework 1
(Due: 04/13)
04-06

04-08
Week 3
  • Chapter 4: Graph concepts (induced-width), Directional consistency, Adaptive-consistency, bucket-elimination.
Chap. 4
Homework 2
(Due: 04/20)

04-13

04-15

Week 4
  • Chapter 5: Backtracking strategies: Look-ahead schemes: forward-checking, variable and value orderings, constraint propagation. The Davis-Putnam algorithms.
Chap. 5
Homework 3
(Due: 04/27)

04-20

04-22
Week 5
  • Chapter 6: Backtracking search, Look-back schemes: backjumping, constraint learning. Current software: CSP and SAT solvers (e.g., MAC,Minisat). AND/OR search spaces
Chap. 6


Homework 4
(Due: 05/04)


04-27

04-29
Week 6
  • Chapter 7: Stochastic local search, SLS, GSAT, WSAT
  • Chapter 8: Advanced consistency methods; relational consistency and bucket-elimination, row-convexity, tightness, looseness, Horn theories. clauses.
Chap. 7



Chap. 8
Homework 5 (updated 5/7)
(Due: 05/11)
Minisat
WALKSAT
RSAT

05-04

05-06
Week 7
  • Chapter 9: Tree Clustering, treewidth and hypertree width
  • Chapter 13: Cosntraint Optimization
Chap. 9

Chap. 13
Homework 6
(updated 5/14, 7pm)
(Due: 05/18)
REES files
05-11

05-13
Week 8
  • Constraint Optimization, (continued)
  • Chapter 10: combining seach and inference, the cycle-cutset scheme, the super cluster scheme. Caching in AND/OR search spaces.
Chap. 10
Homework 7
(Due: 06/01) -- updated 5/29
05-18

05-20
Week 9
  • Holiday on Monday
  • Chapter 10 (continued)


05-25

05-27
Week 10
  • Additional topics as time permits; Temporal constraints (chapter 12) AND/OR search space for knowledge compilations
  • Exam/ project presentations


06-01

06-03
Week 11
  • Project presentations


06-01

06-09

Resources on the Internet