Dr. Rina Dechter - University of California at Irvine ZOT!
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CS 275 - Fall 2020, Constraint Networks
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Instructor: Rina Dechter
Days,Time: M/W, 3:30 pm - 4:50 pm (PT)
Classoom: Zoom Meeting ID: 92451965137 - https://uci.zoom.us/j/92451965137
Office hours: W, 9:00 - 10:00 am
Exam: TBD

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 and satisfiability 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 satisfiability-based schemes will be discussed. This year we will look at the potential collaboration between Constraint Processing and Neural Networks. We ask: can neural networks help constraint processing, and vice-versa, can constraints representation and algorithms be relevant to training neural networks? Students will have an opportunity to address such questions through recent literatures in their projects.

Required textbook: Rina Dechter, Constraint Processing, Morgan Kaufmann

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

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



Week Topic Slides
Additional Material
Week 1
  • Chapters 1,2: Introductions to constraint network model. Graph representations, binary constraint networks.
Set 1
Lec 1
Lec 2
HW 1
Due Thu 10/15
11:59 PM (PT)
OR-Tools Tutorial

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

Lec 3
Lec 4


Week 3
  • Chapter 4: Graph concepts (induced-width), Directional consistency, Adaptive-consistency, bucket-elimination.
Set 3 Lec 5
Lec 6
HW 2
Due Mon 10/26
11:59 PM (PT)
Computing Tree Width

Ordering Schemes


Week 4
  • Chapter 5: Backtracking search: Look-ahead schemes: forward-checking, variable and value orderings. DPLL.
Set 4 Lec 7
Lec 8
HW 3
Due Mon 11/2
11:59 PM (PT)
Constraint Propragation by Christian Bessiere

Complete Algorithms by Darwiche and Pipatsrisawat

Week 5
  • Chapter 6: Backtracking search; Look-back schemes: backjumping, constraint learning. SAT solving and solvers (e.g., MAC, Minisat).
Set 5 Lec 9
Lec 10
HW 4
Due Mon 11/9
11:59 PM (PT)



Week 6
  • Satisfiability Solving
Set 6
Lec 11
HW 5
Due Wed 11/18
11:59 PM (PT)
SAT Handbook CDCL 11-09

11-11 [VET.DAY.]
Week 7
  • Stochastic Local Search.
  • Tree-Decomposition Schemes.
Set 7a
Set 7b
Lec 12
Lec 13
HW 6
Due Wed 12/02
11:59 PM (PT)

Week 8
  • Chapter 13: Constraint Optimization
  • AND/OR Search Spaces
Set 8 Lec 14
Lec 15
AND/OR Search Spaces for Graphical Models 11-23

Week 9
  • Paper Presentations
Pres 1-3
Pres 4-6

Week 10
  • Paper Presentations

Pres 7-9
Pres 10-12
Project Reports
Due Thu 12/17
11:59 PM (PT)

Week 11
  • Final Exam (4-6pm)


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