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ICS-275A, Constraint Networks, Fall 2003
home work | slides | readings | project

  • 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.


Required textbook: Rina Dechter, Constraint Processing, Morgan Kaufmann

Additional sources

  • 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

Grading Policy
Homeworks and projects (60%), Final (40%).

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

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
Week 2
  • Tu, (extended class: 11-1) Chapter 3: Consistency enforcing algorithms, arc, path and i-consistency
  • Th, Chapter 3: continued.
Week 3
  • Tu/Th,Chapter 4: Graph concepts (induced-width), Directional consistency, Adaptive-consistency, bucket-elimination.
Week 4
  • Tu/Th: Chapter 5: Backtracking search, look-ahead methods
Week 5
  • Tu/Th, Chapter 6: Backtracking search, look-back methods
Week 6
  • Tu, Chapter 7: Stochastic local search
  • Th: Chapter 8: Advanced consistency methods; Relational consistency and bucket-elimination
Week 7
  • Tu, Veterans' Day
  • Th, Chapter 9: Tree Clustering
Week 8
  • Tu, Tree Clustering cont.
  • Th, Chapter 10: combining seach and inference, the cycle-cutset scheme, the super cluster scheme.
Week 9
  • Tu, Chapter 13: Constraint optimization
  • Th: Thanksgiving
Week 10
  • Additional topics taken from: As time permits; Temporal constraints (Chapter 12), Tractable languages (chap. 11), Constraint Programming (Chapter 14)
  • Final

Resources on the Internet