[an error occurred while processing this directive]

ICS-275, Constraint Networks, Spring 2007
home work | slides | readings | project

  • Instructor: Rina Dechter
  • Section: 35340
  • Classoom: ICF 101
  • 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 it facilitates many computational tasks relevant to these domains such as constraint satisfaction and constraint optimization. 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 probabilistic Bayes networks.


Textbook

Required textbook: Rina Dechter, Constraint Processing, Morgan Kaufmann

Additional readings


Grading Policy
Homeworks and projects (70%), Final (30%).


Assignments:
There will be weekly homework-assignments, a project, and the final.


Syllabus:
Subject to changes

Week Topic Slides
Homework
Date  
Week 1
  • No class Monday due to Passover holiday
  • Chapters 1,2: Introductions to constraint network model, and general graphical models. Graph representations, properties of binary constraint networks.
Chap.1 and 2
                    
04-02

04-04
Week 2
  • Chapter 3: Consistency enforcing algorithms, arc, path and i-consistency
Chap.  3
Homework 1
(Due: 04/16)
04-09

04-11
Week 3
  • Chapter 4: Graph concepts (induced-width), Directional consistency, Adaptive-consistency, bucket-elimination.
Chap. 4
Homework 2
(Due: 04/23)
Hw2-solutions
04-16

04-18

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: 04/30)
Hw3-solutions
04-23

04-25
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
  • Chapter 7: Stochastic local search, SLS, GSAT, WSAT
Chap. 6

SAT competition
Homework 4
(Due: 05/07)

Hw4-solutions
04-30

05-02
Week 6
  • Chapter 8: Advanced consistency methods; relational consistency and bucket-elimination, row-convexity, tightness, looseness, Horn theories. clauses.
  • Chapter 9: Tree Clustering, treewidth and hypertree width
Chap. 7



Chap.8
Homework 5
(Due: 05/14)
Minisat
WALKSAT
Satz

Langford encodings
05-07

05-09
Week 7
  • Tree Clustering (continued)
  • Chapter 13: Cosntraint Optimization
Chap. 9

Chap. 10

Chap. 13
Homework 6
(Due: 05/21)
05-14

05-16
Week 8
  • Cosntraint Optimization, (continued)
  • Chapter 10: combining seach and inference, the cycle-cutset scheme, the super cluster scheme. Caching in AND/OR search spaces.

Homework 7
(Due: 06/03)
05-21

05-23
Week 9
  • Holiday on Monday
  • Final


05-28

05-30
Week 10
  • Additional topics as time permits; Temporal constraints (Chapter 12), AND/OR search space for knowledge compilations
  • Project presentations


06-04

06-06

Resources on the Internet