Dr. Rina Dechter - University of California at Irvine ZOT!
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CompSci 275 Spring 2014, Constraint Networks
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  • Instructor: Rina Dechter
  • Section: 35300
  • Classoom: ICS 243
  • Days: Monday & Wednesday
  • Time: 11:00 am - 12:20 pm
  • Office hours: Monday 2:00pm - 3:00pm


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


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


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


Syllabus:
Subject to changes

Project Page

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

Numberjack Tutorial / Code Examples
Homework 1
(due 04-09)
03-31

04-02
Week 2
  • Chapter 3: Constraint propagation and consistency enforcing algorithms, arc, path and i-consistency
Set 2

Homework 2
(due 04-16)
Constraint Propragation, by Christian Bessiere 04-07

04-09

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

Lecture
Homework 3
(due 04-23)
04-14

04-16
Week 4
  • Chapter 5: Backtracking search: Look-ahead schemes: forward-checking, variable and value orderings. DPLL.
Set 4 Lecture

Lecture
Homework 4
(due 04-30)
04-21

04-23
Week 5
  • Chapter 6: Backtracking search; Look-back schemes: backjumping, constraint learning. SAT solving and solvers (e.g., MAC, Minisat).
Set 5 Lecture

Homework 5
(due 05-12) Minisat
WALKSAT
RSAT
04-28

04-30
Week 6
  • Chapter 7: Stochastic local search, SLS, GSAT, WSAT
  • Satisfiability solving
Set 6 Lecture

Lecture
05-05

05-07
Week 7
  • Chapter 8: Advanced consistency methods; relational consistency and bucket-elimination, row-convexity, tightness, looseness.
Set 7 Lecture

Lecture
Homework 6
(due 05-21)
05-12

05-14
Week 8
  • Chapter 13: Constraint Optimization, soft constraints
Set 8 Lecture 05-19

05-21
Week 9
  • No class 05/26 - Memorial Day
  • Chapter 13: Constraint Optimization, soft constraints (continued)


Lecture
Homework 7
(due 06-04)
05-26

05-28
Week 10


06-02

06-04
Week 11


06-10


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