Dr. Rina Dechter @ UCI

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.

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		Homework 1 (due 01-14)	Numberjack MiniZinc Code Examples	01-05 01-07

Week 2	Chapter 3: Constraint propagation and consistency enforcing algorithms, arc, path and i-consistency	Set 2	Lecture 3 Lecture 4	Homework 2 (due 01-21)	Constraint Propragation by Christian Bessiere	01-12 01-14

Week 3	Chapter 4: Graph concepts (induced-width), Directional consistency, Adaptive-consistency, bucket-elimination.	Set 3	Lecture 5 Lecture 6	Homework 3 (due 01-28)	The Sat Solving Revolution: Solving, Sampling and Counting by Moshe Vardi	01-19 01-21

Week 4	Chapter 5: Backtracking search: Look-ahead schemes: forward-checking, variable and value orderings. DPLL.	Set 4	Lecture 7 Lecture 8	Homework 4 (due 02-04)	Complete Algorithms by Darwiche and Pipatsrisawat	01-26 01-28

Week 5	Chapter 6: Backtracking search; Look-back schemes: backjumping, constraint learning. SAT solving and solvers (e.g., MAC, Minisat).	Set 5	Lecture 9 Lecture 10	Homework 5 (due 02-11)	Minisat WALKSAT RSAT	02-02 02-04

Week 6	Chapter 7: Stochastic local search, SLS, GSAT, WSAT Satisfiability solving	Satisfiability Set 6	Lecture 11 Lecture 12		SATHandbook-CDCL	02-09 02-11

Week 7	Chapter 8: Advanced consistency methods; relational consistency and bucket-elimination, row-convexity, tightness, looseness.	Set 7 Set 8	Lecture 13 Lecture 14	Homework 6	AND/OR Search Spaces for Graphical Models	02-16 02-18

Week 8	Chapter 13: Constraint Optimization, soft constraints	Set 9	Lecture 15 Lecture 16	Homework 7		02-23 02-25

Week 9	Chapter 13: Constraint Optimization, soft constraints (continued)		Exam, in class			03-01 03-03

Week 10
Week 11

Dr. Rina Dechter - University of California at Irvine
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CompSci 275 Winter 2016, Constraint Networks

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