constraintSat

constraintSat - ConstraintSatisfactionProblems(CSPs)

Info iconThis preview shows pages 1–9. Sign up to view the full content.

View Full Document Right Arrow Icon
Constraint Satisfaction Problems (CSPs) Chapter 6.1 – 6.4, except 6.3.3
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Outline What is a CSP Backtracking for CSP Local search for CSPs
Background image of page 2
You Will Be Expected to Know Basic definitions (section 6.1) Arc consistency (6.2)  (node and path consistency removed) Backtracking search (6.3) Variable and value ordering: minimum-remaining values, degree  heuristic, least-constraining-value (6.3.1) Forward checking (6.3.2) Local search for CSPs: min-conflict heuristic (6.4)
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Constraint Satisfaction Problems What is a CSP? Finite set of variables  X 1 , X 2 , …, X n Nonempty domain of possible values for each variable  D 1 , D 2 , …, D n Finite set of constraints  C 1 , C 2 , …, C m Each constraint  C i  limits the values that variables can take,  e.g.,  X ≠ X 2 Each constraint  C i  is a pair <scope, relation> Scope = Tuple of variables that participate in the constraint. Relation = List of allowed combinations of variable values. May be an explicit list of allowed combinations. May be an abstract relation allowing membership testing and listing.
Background image of page 4
Sudoku as a Constraint Satisfaction Problem (CSP) Variables: 81 variables A1, A2, A3, …, I7, I8, I9 Letters index rows, top to bottom Digits index columns, left to right Domains: The nine positive digits A1   {1, 2, 3, 4, 5, 6, 7, 8, 9} Etc. Constraints: 27  Alldiff  constraints Alldiff (A1, A2, A3, A4, A5, A6, A7, A8, A9) Etc. A B C D E F G H I 1 2 3 4 5 6 7 8 9
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
CSPs --- what is a solution? state  is an  assignment  of values to some or all variables. An assignment is  complete  when every variable has a value.  An assignment is  partial  when some variables have no values. Consistent assignment assignment does not violate the constraints solution  to a CSP is a complete and consistent assignment. Some CSPs require a solution that maximizes an  objective function Examples of Applications:  Scheduling the time of observations on the Hubble Space Telescope Airline schedules  Cryptography Computer vision -> image interpretation Scheduling your MS or PhD thesis exam 
Background image of page 6
CSP example: map coloring Variables:  WA, NT, Q, NSW, V, SA, T Domains:  D i ={red,green,blue} Constraints:adjacent regions must have different colors. E.g.  WA   NT   
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Solutions are assignments satisfying all constraints, e.g.  
Background image of page 8
Image of page 9
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 10/22/2011 for the course CS CS 2710 taught by Professor Wiebe during the Fall '11 term at Pittsburgh.

Page1 / 57

constraintSat - ConstraintSatisfactionProblems(CSPs)

This preview shows document pages 1 - 9. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online