|Did you know ...||Search Documentation:|
|Example: Eight queens puzzle|
We illustrate the concepts of the preceding sections by means of the so-called eight queens puzzle. The task is to place 8 queens on an 8x8 chessboard such that none of the queens is under attack. This means that no two queens share the same row, column or diagonal.
To express this puzzle via CLP(FD) constraints, we must first pick a suitable representation. Since CLP(FD) constraints reason over integers, we must find a way to map the positions of queens to integers. Several such mappings are conceivable, and it is not immediately obvious which we should use. On top of that, different constraints can be used to express the desired relations. For such reasons, modeling combinatorial problems via CLP(FD) constraints often necessitates some creativity and has been described as more of an art than a science.
In our concrete case, we observe that there must be exactly one queen per column. The following representation therefore suggests itself: We are looking for 8 integers, one for each column, where each integer denotes the row of the queen that is placed in the respective column, and which are subject to certain constraints.
In fact, let us now generalize the task to the so-called N queens
puzzle, which is obtained by replacing 8 by N everywhere it
occurs in the above description. We implement the above considerations
n_queens/2, where the first argument
is the number of queens (which is identical to the number of rows and
columns of the generalized chessboard), and the second argument is a
list of N integers that represents a solution in the form
n_queens(N, Qs) :- length(Qs, N), Qs ins 1..N, safe_queens(Qs). safe_queens(). safe_queens([Q|Qs]) :- safe_queens(Qs, Q, 1), safe_queens(Qs). safe_queens(, _, _). safe_queens([Q|Qs], Q0, D0) :- Q0 #\= Q, abs(Q0 - Q) #\= D0, D1 #= D0 + 1, safe_queens(Qs, Q0, D1).
Note that all these predicates can be used in all directions: We can use them to find solutions, test solutions and complete partially instantiated solutions.
The original task can be readily solved with the following query:
?- n_queens(8, Qs), label(Qs). Qs = [1, 5, 8, 6, 3, 7, 2, 4] .
Using suitable labeling strategies, we can easily find solutions with 80 queens and more:
?- n_queens(80, Qs), labeling([ff], Qs). Qs = [1, 3, 5, 44, 42, 4, 50, 7, 68|...] . ?- time((n_queens(90, Qs), labeling([ff], Qs))). % 5,904,401 inferences, 0.722 CPU in 0.737 seconds (98% CPU) Qs = [1, 3, 5, 50, 42, 4, 49, 7, 59|...] .
Experimenting with different search strategies is easy because we have separated the core relation from the actual search.