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# Constraint Satisfaction Problems

Constraint Satisfaction Problems. Russell and Norvig: Chapter 3, Section 3.7 Chapter 4, Pages 104-105 Slides adapted from: robotics.stanford.edu/~latombe/cs121/2003/home.htm. Intro Example: 8-Queens. Generate-and-test, with no redundancies  “only” 8 8 combinations. Download Presentation ## Constraint Satisfaction Problems

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1. Constraint Satisfaction Problems Russell and Norvig: Chapter 3, Section 3.7Chapter 4, Pages 104-105 Slides adapted from: robotics.stanford.edu/~latombe/cs121/2003/home.htm

2. Intro Example: 8-Queens Generate-and-test, with no redundancies  “only” 88 combinations

3. Intro Example: 8-Queens

4. What is Needed? • Not just a successor function and goal test • But also a means to propagate the constraints imposed by one queen on the others and an early failure test •  Explicit representation of constraints and constraint manipulation algorithms

5. Constraint Satisfaction Problem • Set of variables {X1, X2, …, Xn} • Each variable Xi has a domain Di of possible values • Usually Di is discrete and finite • Set of constraints {C1, C2, …, Cp} • Each constraint Ck involves a subset of variables and specifies the allowable combinations of values of these variables • Assign a value to every variable such that all constraints are satisfied

6. Example: 8-Queens Problem • 8 variables Xi, i = 1 to 8 • Domain for each variable {1,2,…,8} • Constraints are of the forms: • Xi = k  Xj k for all j = 1 to 8, ji • Xi = ki, Xj= kj |i-j| | ki - kj| • for all j = 1 to 8, ji

7. NT Q WA SA NT NSW Q V WA SA T NSW V T Example: Map Coloring • 7 variables {WA,NT,SA,Q,NSW,V,T} • Each variable has the same domain {red, green, blue} • No two adjacent variables have the same value: • WANT, WASA, NTSA, NTQ, SAQ, SANSW, SAV,QNSW, NSWV

8. T1 T2 T4 T3 Example: Task Scheduling T1 must be done during T3 T2 must be achieved before T1 starts T2 must overlap with T3 T4 must start after T1 is complete

9. Binary constraints NT T1 Q WA T2 NSW T4 SA V T3 T Constraint Graph Two variables are adjacent or neighbors if they are connected by an edge or an arc

10. CSP as a Search Problem • Initial state: empty assignment • Successor function: a value is assigned to any unassigned variable, which does not conflict with the currently assigned variables • Goal test: the assignment is complete • Path cost: irrelevant

11. Example: Map Coloring s0 = ??????? successors(s0) = {R??????, G??????, B??????} What search algorithms could we use? BFS? DFS?

12. Remark • Finite CSP include 3SAT as a special case • 3SAT is known to be NP-complete • So, in the worst-case, we cannot expect to solve a finite CSP in less than exponential time

13. {} NT Q WA WA=red WA=green WA=blue SA NSW V WA=red NT=green WA=red NT=blue T WA=red NT=green Q=red WA=red NT=green Q=blue Map Coloring

14. Backtracking Algorithm CSP-BACKTRACKING(PartialAssignment a) • If a is complete then return a • X select an unassigned variable • D  select an ordering for the domain of X • For each value v in D do • If v is consistent with a then • Add (X= v) to a • result CSP-BACKTRACKING(a) • If resultfailure then return result • Return failure CSP-BACKTRACKING({})

15. Questions • Which variable X should be assigned a value next? • In which order should its domain D be sorted? • What are the implications of a partial assignment for yet unassigned variables?

16. NT WA NT Q WA SA SA NSW V T Choice of Variable • Map coloring

17. Choice of Variable • 8-queen

18. Choice of Variable Most-constrained-variable heuristic: Select a variable with the fewest remaining values

19. NT Q WA SA SA NSW V T Choice of Variable Most-constraining-variable heuristic: Select the variable that is involved in the largest number of constraints on other unassigned variables

20. NT WA NT Q WA SA NSW V {} T Choice of Value

21. NT WA NT Q WA SA NSW V {blue} T Choice of Value Least-constraining-value heuristic: Prefer the value that leaves the largest subset of legal values for other unassigned variables

22. Constraint Propagation … … is the process of determining how the possible values of one variable affect the possible values of other variables

23. Forward Checking After a variable X is assigned a value v, look at each unassigned variable Y that is connected to X by a constraint and deletes from Y’s domain any value that is inconsistent with v

24. NT Q WA NSW SA T V Map Coloring

25. NT Q WA NSW SA T V Map Coloring

26. NT Q WA NSW SA T V Map Coloring

27. NT Q WA NSW SA T V Impossible assignments that forward checking do not detect Map Coloring

28. Edge Labeling in Computer Vision Russell and Norvig: Chapter 24, pages 745-749

29. Edge Labeling

30. Edge Labeling

31. Labels of Edges • Convex edge: • two surfaces intersecting at an angle greater than 180° • Concave edge • two surfaces intersecting at an angle less than 180° • + convex edge, both surfaces visible • − concave edge, both surfaces visible •  convex edge, only one surface is visible and it is on the right side of 

32. Edge Labeling

33. + + + + + - - + + + + + Edge Labeling

34. - - + + - + + - - + + - - - - + + + - + Junction Label Sets (Waltz, 1975; Mackworth, 1977)

35. Edge Labeling as a CSP • A variable is associated with each junction • The domain of a variable is the label set of the corresponding junction • Each constraint imposes that the values given to two adjacent junctions give the same label to the joining edge

36. - + + - - + + - + Edge Labeling

37. + - - + + - - - - + + Edge Labeling

38. + + + + - + + - - + + Edge Labeling + +

39. - - + + + + + - - Edge Labeling + +

40. Removal of Arc Inconsistencies REMOVE-ARC-INCONSISTENCIES(J,K) • removed  false • X  label set of J • Y  label set of K • For every label y in Y do • If there exists no label x in X such that the constraint (x,y) is satisfied then • Remove y from Y • If Y is empty then contradiction  true • removed  true • Label set of K  Y • Return removed

41. CP Algorithm for Edge Labeling • Associate with every junction its label set • Q stack of all junctions • while Q is not empty do • J UNSTACK(Q) • For every junction K adjacent to J do • If REMOVE-ARC-INCONSISTENCIES(J,K) then • If K’s domain is non-empty then STACK(K,Q) • Else return false (Waltz, 1975; Mackworth, 1977)

42. General CP for Binary Constraints Algorithm AC3 • contradiction false • Q stack of all variables • while Q is not empty and not contradiction do • X UNSTACK(Q) • For every variable Y adjacent to X do • If REMOVE-ARC-INCONSISTENCIES(X,Y) then • If Y’s domain is non-empty then STACK(Y,Q) • Else return false

43. Complexity Analysis of AC3 • e = number of constraints (edges) • d = number of values per variable • Each variables is inserted in Q up to d times • REMOVE-ARC-INCONSISTENCY takes O(d2) time • CP takes O(ed3) time

44. X1 {1,2,3,4} X2 {1,2,3,4} 1 2 3 4 1 2 3 4 X3 {1,2,3,4} X4 {1,2,3,4} Is AC3 All What is Needed?

45. Solving a CSP • Search: • can find good solutions, but must examine non-solutions along the way • Constraint Propagation: • can rule out non-solutions, but this is not the same as finding solutions: • Interweave constraint propagation and search • Perform constraint propagation at each search step.

46. 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4

47. X1 {1,2,3,4} X2 {1,2,3,4} 1 2 3 4 1 2 3 4 X3 {1,2,3,4} X4 {1,2,3,4} 4-Queens Problem

48. X1 {1,2,3,4} X2 {1,2,3,4} 1 2 3 4 1 2 3 4 X3 {1,2,3,4} X4 {1,2,3,4} 4-Queens Problem

49. X1 {1,2,3,4} X2 {1,2,3,4} 1 2 3 4 1 2 3 4 X3 {1,2,3,4} X4 {1,2,3,4} 4-Queens Problem

50. X1 {1,2,3,4} X2 {1,2,3,4} 1 2 3 4 1 2 3 4 X3 {1,2,3,4} X4 {1,2,3,4} 4-Queens Problem

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