- Instructor: Rina Dechter
- Section: 36810
- Classoom: CS 253
- Days: Tuesday & Thursday
- Time: 11:00 - 12:20 pm
Course Goals
The purpose of this course is to familiarize students
with the theory and techniques of constraint processing, using the
constraint network model. This model offers a natural language for
encoding world knowledge in areas such as scheduling, vision,
diagnosis, prediction and design, and it facilitates many computational
tasks relevant to these domains. The course will focus on techniques
for constraint processing. It will cover search techniques, consistency
algorithms and structure based techniques, and will focus on properties
that facilitate efficient solutions. Extensions will be given into
applications such as temporal reasoning, diagnosis, scheduling, and
probabilistic Bayes networks. The topics covered will be taken from the
following list.
Readings
Required textbook: Rina Dechter, Constraint Processing, Morgan Kaufmann
Additional sources
Outline
- The constraint network model, examples. Graph
representations, Properties of binary networks: equivalence, the
minimal and the projection networks.
- Approximation algorithms: local-consistency vs.
global-consistency, arc and path-consistency, directional-consistency,
adaptive-consistency, relational-consistency, bucket-elimination.
- Backtracking strategies: Look-ahead schemes:
forward-checking, variable and value orderings, constraint propagation.
The Davis-Putnam algorithms.
Look-back schemes: backjumping, constraint learning.
- Stochastic local search algorithms: SLS, GSAT, WSAT
- Constraint-based tractable classes: row-convexity,
tightness, looseness, implicational and functional constraints, Horn
clauses.
- Topology-based concepts and algorithms, tree-clustering,
bucket-elimination.
- Hybrids of search and inference; the (cycle)-cutset scheme,
the w-cutset scheme, the seperator-based scheme, and the time-space
tradeoff.
- Constraint optimization.
- Constraint Logic Programming
Homeworks and projects (60%), Final (40%).
Assignments:
There will be weekly homework-assignments, a
project, and the final.
Syllabus:
Subject to changes
| Week |
Topic |
Date |
 |
| Week 1 |
- Tu, (extended class: 11-1), Chapters 1,2: Introduction:
examples
and definitions of Constraint networks. Propertries
of Binary networks.
- Th: No Class
|
09-30 |
|
| Week 2 |
- Tu, (extended class: 11-1) Chapter 3:
Consistency enforcing
algorithms, arc, path and i-consistency
- Th, Chapter 3: continued.
|
10-07 |
|
| Week 3 |
- Tu/Th,Chapter 4: Graph concepts
(induced-width), Directional consistency, Adaptive-consistency,
bucket-elimination.
|
10-14 |
|
| Week 4 |
- Tu/Th: Chapter 5: Backtracking
search, look-ahead methods
|
10-21 |
|
| Week 5 |
- Tu/Th, Chapter 6: Backtracking
search, look-back methods
|
10-28 |
|
| Week 6 |
- Tu, Chapter 7: Stochastic local search
- Th: Chapter 8: Advanced consistency methods; Relational consistency
and bucket-elimination
|
10-04 |
|
| Week 7 |
- Tu, Veterans' Day
- Th, Chapter 9: Tree Clustering
|
11-11 |
|
| Week 8 |
- Tu, Tree Clustering cont.
- Th, Chapter 10: combining seach
and inference, the cycle-cutset scheme, the super cluster scheme.
|
11-18 |
|
| Week 9 |
- Tu, Chapter 13: Constraint
optimization
- Th: Thanksgiving
|
11-25 |
|
| Week 10 |
- Additional topics taken from:
As time permits; Temporal constraints (Chapter 12), Tractable languages
(chap. 11), Constraint Programming (Chapter 14)
- Final
|
11-02 |
Resources on the
Internet
|
