1 / 58

Examples for Discrete Constraint Programming

Examples for Discrete Constraint Programming. Belaid MOA UVIC, SHRINC Project. Examples. Map coloring Problem Cryptarithmetic N-queens problem Magic sequence Magic square Zebra puzzle Uzbekian puzzle A tiny transportation problem Knapsack problem graceful labeling problem.

ira-stuart
Download Presentation

Examples for Discrete Constraint Programming

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Examples for Discrete Constraint Programming Belaid MOA UVIC, SHRINC Project

  2. Examples • Map coloring Problem • Cryptarithmetic • N-queens problem • Magic sequence • Magic square • Zebra puzzle • Uzbekian puzzle • A tiny transportation problem • Knapsack problem • graceful labeling problem

  3. Map Coloring Problem (MCP) • Given a map and given a set of colors, the problem is how to color the map so that the regions sharing a boundary line don’t have the same color.

  4. Map Coloring Problem (MCP) • MCP can be viewed as a graph coloring problem: a b b c a d c d

  5. Modeling MCP • MCP can be Modeled as a CSP: • A set of variables representing the color of each region • The domain of the variables is the set of the colors used. • A set of constraints expressing the fact that countries that share the same boundary are not colored with the same color.

  6. Modeling (MCP) b d a c e f g • Example - Australia • Variables = {a,b,c,d,e,f,g} • Domain = {red, blue, green} • Constraints: • a!=b, a!=c • b!=c, b!=d • c!=e, c!=f • e!=d, e!=f

  7. Coding MCP • OPL studio - Ilog Solver: 54 solutions found enum Country={a,b,c,d,e,f,g}; enum Color = {red, green, blue}; var Color color[Country]; solve{ color[a]<>color[b];color[a]<>color[c]; color[b]<>color[c];color[b]<>color[d]; color[c]<>color[e];color[c]<>color[f]; color[e]<>color[d];color[e]<>color[f]; color[b]<>color[c];color[b]<>color[d]; };

  8. Coding MCP

  9. Coding MCP • ECLiPSe :- lib(fd). coloured(Countries) :- Countries=[A,B,C,D,E,F,G], Countries :: [red,green,blue], ne(A,B), ne(A,C), ne(B,C), ne(B,D), ne(C,E), ne(C,F), ne(E,D), ne(E,F), labeling(Countries). ne(X,Y) :- X##Y.

  10. Coding MCP

  11. Cryptarithmetic • The problem is to find the digits corresponding to the letters involved in the following puzzle: SEND + MORE MONEY

  12. Cryptarithmetic Modeling • A CSP model for Cryptarithmetic problem: • Variables={S,E,N,D,M,O,R,Y} • Domain={0,1,2,3,4,5,6,7,8,9} • Constraints • The variables are all different • S!=0, M!=0 • S*103+E*102+N*10+D {SEND} + M*103+O*102+R*10+E {MORE}= M*104+O*103+N*102+E*10+Y {MONEY}

  13. Coding Cryptarithmetic • OPL Studio - ILog Solver enum letter = {S,E,N,D,M,O,R,Y}; range Digit 0..9; var Digit value[letter]; solve{ alldifferent(value); value[S]<>0; value[M] <>0; value[S]*1000+value[E]*100+value[N]*10+value[D]+ value[M]*1000+value[O]*100+value[R]*10+value[E] = value[M]*10000+value[O]*1000+value[N]*100+value[E]*10+value[Y] };

  14. Coding Cryptarithmetic

  15. Coding Cryptarithmetic • ECLiPSe :- lib(fd). sendmore(Digits) :- Digits = [S,E,N,D,M,O,R,Y], Digits :: [0..9], alldifferent(Digits), S #\= 0, M #\= 0, 1000*S + 100*E + 10*N + D + 1000*M + 100*O + 10*R + E #= 10000*M + 1000*O + 100*N + 10*E + Y, labeling(Digits).

  16. Coding Cryptarithmetic

  17. N-Queens Problem • The problem is to put N queens on a board of NxN such that no queen attacks any other queen. • A queen moves vertically, horizontally and diagonally

  18. Modeling N-Queens Problem • The queens problem can be modeling via the following CSP • Variables={Q1,Q2,Q3,Q4,...,QN}. • Domain={1,2,3,…,N} represents the column in which the variables can be. • Constraints • Queens not on the same row: already taken care off by the good modeling of the variables. • Queens not on the same column: Qi != Qj • Queens not on the same diagonal: |Qi-Qj| != |i-j|

  19. Coding N-Queens Problem • OPL Studio - ILog Solver int n << "number of queens:"; range Domain 1..n; var Domain queens[Domain]; solve { alldifferent(queens); forall(ordered i,j in Domain) { abs(queens[i]-queens[j])<> abs(i-j) ; }; };

  20. Coding N-Queens Problem

  21. Coding N-Queens Problem • ECLiPSe :-lib(fd). nqueens(N, Q):- length(Q,N), Q::1..N, alldifferent(Q), ( fromto(Q, [Q1|Cols], Cols, []) do ( foreach(Q2, Cols), param(Q1), count(Dist,1,_) do Q2 - Q1 #\= Dist, Q1 - Q2 #\= Dist ) ), search(Q). search([]). search(Q):- deleteff(Var,Q,R), indomain(Var), search(R).

  22. Coding N-Queens Problem

  23. Magic sequence problem • Given a finite integer n, the problem consists of finding a sequence S = (s0,s1,…,sn), such that si represents the number of occurrences of i in S. • Example: • (2, 0, 2, 0) • (1,2,1,0)

  24. Modeling MSP • MSP can be modeled by the following CSP • variables: s0,s1, …,sn-1 • Domain:{0,1,…,n} • constraints: • number of occurences of i in (s0,s1, …,sn-1) is si. • Redundant constraints: • the sum of s0,s1, …,sn-1 is n • the sum of i*Si, i in [0..n-1] is n

  25. Coding MSP • OPL - ILog Solver int n << "Number of Variables:"; range Range 0..n-1; range Domain 0..n; int value[i in Range ] = i; var Domain s[Range]; solve { distribute(s,value,s); //global constraint s[i]=sum(j in Range)(s[j]=i) sum(i in Range) s[i] = n; //redundant constraint sum(i in Range) s[i]*i = n; //redundant constraint };

  26. Coding MSP • ECLiPSe :- lib(fd). :- lib(fd_global). :- lib(fd_search). solve(N, Sequence) :- length(Sequence, N), Sequence :: 0..N-1, ( for(I,0,N-1), foreach(Xi, Sequence), foreach(I, Range), param(Sequence) do occurrences(I, Sequence, Xi) ), N #= sum(Sequence), % two redundant constraints N #= Sequence*Range, search(Sequence, 0, first_fail, indomain, complete, []).%search procedure

  27. Magic square problem • A magic square of size N is an NxN square filled with the numbers from 1 to N2 such that the sums of each row, each column and the two main diagonals are equal. • Example:

  28. Modeling Magic square pb • Magic square problem can be viewed as a CSP with the following properties: • Variables: the elements of the matrix representing the square • Domain: 1..N*N • Constraints: • magic sum = sum of the columns = sum of the rows = sum of the down diagonal = sum of the up diagonal • Remove symmetries • Redundant constraint: • magic sum = N(N2+1)/2

  29. Coding Magic square pb • OPL Studio: int N << "The length of the square:"; range Dimension 1..N; range Values 1..N*N; int msum = N*(N*N+1)/2; //magic sum var Values square[1..N,1..N]; solve { //The elements of the square are all different alldifferent(square); //the sum of the diagonal is magic sum msum = sum(i in Dimension)square[i,i]; //the sum of the up diagonal is magic sum

  30. Coding Magic square pb msum = sum(i in Dimension)square[i,N-i+1]; //the sum of the rows and columns are all magic sum forall(i in Dimension){ msum = sum(j in Dimension)square[i,j]; msum = sum(k in Dimension)square[k,i]; }; //remove symmetric behavior of the square square[N,1] < square[1,N]; square[1,1] < square[1,N]; square[1,1] < square[N,N]; square[1,1] < square[N,1]; };

  31. Magic square problem

  32. Coding Magic square pb • ECLiPSe: :- lib(fd). magic(N) :- Max is N*N, Magicsum is N*(Max+1)//2, dim(Square, [N,N]), Square[1..N,1..N] :: 1..Max, Rows is Square[1..N,1..N], flatten(Rows, Vars), alldifferent(Vars),

  33. Coding Magic square pb %constraints on rows ( for(I,1,N), foreach(U,UpDiag), foreach(D,DownDiag), param(N,Square,Sum) do Magicsum #= sum(Square[I,1..N]), Magicsum #= sum(Square[1..N,I]), U is Square[I,I], D is Square[I,N+1-I] ), %constraints on diagonals Magicsum #= sum(UpDiag), Magicsum #= sum(DownDiag),

  34. Coding Magic square pb %remove symmetry Square[1,1] #< Square[1,N], Square[1,1] #< Square[N,N], Square[1,1] #< Square[N,1], Square[1,N] #< Square[N,1], %search labeling(Vars), print(Square). Unfortunately, ECLiPSe ran for a long time without providing any answer. ECLiPSe had also the same behavior for a similar code from ECLiPSe website.

  35. A Tiny Transportation problem x 11 x x 12 1n x 2 21 a 2 b x 2 2 22 x 2n • How much should be shipped from several sources to several destinations Demand Qty Supply cpty Source Qty Shipped Destination a 1 b 1 1 1 : : : : n a m b m n

  36. A Tiny Transportation problem 2 2 3 • 3 plants with known capacities, 4 clients with known demands and transport costs per unit between them. 200 500 1 1 400 Find qty shipped? 300 300 3 400 4 100

  37. Modeling TTP • TTP can be modeled by a CSP with optimization as follows: • Variables: A1,A2,A3,B1,B2,B3,C1, C2, C3,D1,D2,D3 • Domain: 0.0..Inf • constraints: • Demand constraints: A1+A2+A3=200; B1+B2+B3=400; C1+C2+C3=300; D1+D2+D3=100; • Capacity constraints: A1+B1+C1+D1500; A2+B2+C2+D2300; A3+B3+C3+D3400; • Minimization of the objective function: 10*A1 + 7*A2 + 11*A3 + 8*B1 + 5*B2 + 10*B3 + 5*C1 + 5*C2 + 8*C3 + 9*D1 + 3*D2 + 7*D3

  38. Coding TTP • OPL Studio - Cplex Solver var float+ productA[Range];var float+ productB[Range]; var float+ productC[Range]; var float+ productD[Range]; minimize 10*productA[1] + 7*productA[2] + 11*productA[3] + 8*productB[1] + 5*productB[2] + 10*productB[3] + 5*productC[1] + 5*productC[2] + 8*productC[3] + 9*productD[1] + 3*productD[2] + 7*productD[3] subject to { productA[1] + productA[2] + productA[3] = 200; productB[1] + productB[2] + productB[3] = 400; productC[1] + productC[2] + productC[3] = 300; productD[1] + productD[2] + productD[3] = 100; productA[1] + productB[1] + productC[1] + productD[1]<=500 productA[2] + productB[2] + productC[2]+productC[2] <= 300; productA[3] + productB[3] + productC[3] + productD[3] <= 400; };

  39. Coding TTP • OPL Studio - Cplex Solver: Solution found Optimal Solution with Objective Value: 6200.0000 productA[1] = 100.0000 productA[2] = 0.0000 productA[3] = 100.0000 productB[1] = 100.0000 productB[2] = 300.0000 productB[3] = 0.0000 productC[1] = 300.0000 productC[2] = 0.0000 productC[3] = 0.0000 productD[1] = 0.0000 productD[2] = 100.0000 productD[3] = 0.0000

  40. Coding TTP • ECLiPSe :- lib(eplex_cplex). main1(Cost, Vars) :- Vars = [A1, A2, A3, B1, B2, B3, C1, C2, C3, D1, D2, D3], Vars :: 0.0..inf, A1 + A2 + A3 $= 200, B1 + B2 + B3 $= 400, C1 + C2 + C3 $= 300, D1 + D2 + D3 $= 100, A1 + B1 + C1 + D1 $=< 500, A2 + B2 + C2 + D2 $=< 300, A3 + B3 + C3 + D3 $=< 400, optimize(min(10*A1 + 7*A2 + 11*A3 + 8*B1 + 5*B2 + 10*B3 + 5*C1 + 5*C2 + 8*C3 +9*D1 + 3*D2 + 7*D3), Cost). It didn’t run! calling an undefined procedure cplex_prob_init(…)

  41. Zebra puzzle • Zebra is a well known puzzle in which five men with different nationalities live in the first five house of a street. They practice five distinct professions, and each of them has a favorite animal and a favorite drink, all of them different. The five houses are painted in different colors. The puzzle is to find who owns Zebra. • Zebra puzzle has different statements. We’ll handle two. • This kind of Puzzle is usually treated as an instance of the class of tabular constraint satisfaction problems in which we express the problem using tables. • In this presentation we show how to solve it using CSP languages: OPL studio and ECLiPSe.

  42. Modeling Zebra puzzle • In crucial step in Modeling Zebra puzzle is finding the decision variables. In our case, we consider: • variables: persons, colors, pets, drinks, tobaccos. • Domain: 1..5 (representing the five houses) • Constraints: Expressed directly from the statement of the puzzle.

  43. Coding Zebra puzzle • OPL Studio - ILog Solver enum people {Englishman, Spaniard, Ukrainian, Norwegian, Japanese}; enum drinks {coffee, tea, milk, orange, water}; enum pets {dog, fox, zebra, horse, snails}; enum tabacco {winston, kools, chesterfields, luckyStrike, parliaments }; enum colors {ivory, yellow, red , green, blue}; range houses 1..5;//{first, second, third, fourth, fifth}; var houses hseClr[colors]; var houses hsePple[people]; var houses hsePts[pets]; var houses hseDrks[drinks]; var houses hseTaba[tabacco];

  44. Coding Zebra puzzle solve{ hsePple[Englishman] = hseClr[red]; hsePple[Spaniard]=hsePts[dog]; hseDrks[coffee] = hseClr[green]; hsePple[Ukrainian] = hseDrks[tea]; hseClr[green] = hseClr[ivory]+1; hseTaba[winston] = hsePts[snails]; hseTaba[kools]=hseClr[yellow]; hseDrks[milk]=3; hsePple[Norwegian] = 1; hseTaba[chesterfields]=hsePts[fox]+1 \/ hseTaba[chesterfields]=hsePts[fox]-1; hseTaba[kools] = hsePts[horse]+1 \/ hseTaba[kools] = hsePts[horse]-1 ; hseTaba[ luckyStrike] = hseDrks[orange]; hsePple[ Japanese] = hseTaba[parliaments]; hsePple[Norwegian] = hseClr[blue]+1 \/ hsePple[Norwegian] = hseClr[blue]-1; //slient constraints-Global constraint alldifferent alldifferent(hsePple);//forall(ordered i,j in people) hsePple[i] <> hsePple[j]; alldifferent(hsePts);//forall(ordered i,j in pets) hsePts[i] <> hsePts[j]; alldifferent(hseClr);//forall(ordered i,j in colors) hseClr[i] <> hseClr[j]; alldifferent(hseDrks);//forall(ordered i,j in drinks) hseDrks[i] <> hseDrks[j]; alldifferent(hseTaba);//forall(ordered i,j in tabacco) hseTaba[i] <> hseTaba[j]; };

  45. Coding Zebra puzzle % The Englishman lives in a red house. % The Spaniard owns a dog. % The Japanese is a painter. % The Italian drinks tea. % The Norwegian lives in the first house on the left. % The owner of the green house drinks coffee. % The green house is on the right of the white one. % The sculptor breeds snails. % The diplomat lives in the yellow house. % Milk is drunk in the middle house. % The Norwegian's house is next to the blue one. % The violinist drinks fruit juice. % The fox is in a house next to that of the doctor. % The horse is in a house next to that of the diplomat. % % Who owns a Zebra, and who drinks water? %

  46. Coding Zebra puzzle :- lib(fd). zebra :- % we use 5 lists of 5 variables each Nat = [English, Spaniard, Japanese, Italian, Norwegian], Color = [Red, Green, White, Yellow, Blue], Profession = [Painter, Sculptor, Diplomat, Violinist, Doctor], Pet = [Dog, Snails, Fox, Horse, Zebra], Drink = [Tea, Coffee, Milk, Juice, Water], % domains: all the variables range over house numbers 1 to 5 Nat :: 1..5, Color :: 1..5, Profession :: 1..5, Pet :: 1..5, Drink :: 1..5,

  47. Coding Zebra puzzle % the values in each list are exclusive alldifferent(Nat), alldifferent(Color), alldifferent(Profession), alldifferent(Pet), alldifferent(Drink), % and here follow the actual constraints English = Red, Spaniard = Dog, Japanese = Painter, Italian = Tea, Norwegian = 1, Green = Coffee, Green #= White + 1, Sculptor = Snails, Diplomat = Yellow, Milk = 3, Dist1 #= Norwegian - Blue, Dist1 :: [-1, 1], Violinist = Juice, Dist2 #= Fox - Doctor, Dist2 :: [-1, 1], Dist3 #= Horse - Diplomat, Dist3 :: [-1, 1],

  48. Coding Zebra puzzle % put all the variables in a single list flatten([Nat, Color, Profession, Pet, Drink], List), % search: label all variables with values labeling(List), % print the answers: we need to do some decoding NatNames = [English-english, Spaniard-spaniard, Japanese-japanese, Italian-italian, Norwegian-norwegian], memberchk(Zebra-ZebraNat, NatNames), memberchk(Water-WaterNat, NatNames), printf("The %w owns the zebra%n", [ZebraNat]), printf("The %w drinks water%n", [WaterNat]). Answer from ECLiPSe after 0.02s The japanese owns the zebra The norwegian drinks water

  49. Uzbekian Puzzle • An uzbekian sales man met five traders who live in five different cities. The five traders are: • {Abdulhamid, Kurban,Ruza, Sharaf, Usman} The five cities are : • {Bukhara, Fergana, Kokand, Samarkand, Tashkent} Find the order in which he visited the cities given the following information: • He met Ruza before Sharaf after visiting Samarkand, • He reached Fergana after visiting Samarkand followed by other two cities, • The third trader he met was Tashkent, • Immediately after his visit to Bukhara, he met Abdulhamid • He reached Kokand after visiting the city of Kurban followed by other two cities;

  50. Modeling Uzbekian Puzzle • The uzbekian puzzle can formulated within the CSP framework as follows: • Variables: order in which he visited each city and met each trader • Domain:1..5 • constraints: • He met Ruza before Sharaf after visiting Samarkand, • He reached Fergana after visiting Samarkand followed by other two cities, • The third trader he met was Tashkent, • Immediately after his visit to Bukhara, he met Abdulhamid • He reached Kokand after visiting the city of Kurban followed by other two cities;

More Related