A constraint satisfaction problem may be specified as a finite collection of variables, their finite domains of values, and a binary predicate constraining the variables pairwise. When this is the case, the forward constraint propagation algorithm applies.

This function implements the algorithm. Let var be the type representing your variables (or variable IDs), and value be the type of their values; thus (var,value) represents binding a value to a variable. You provide:

• The binary predicate. This predicate evaluates to true iff two bindings conflict with each other, i.e., disallowed by your constraints. There is no need to worry about two bindings mentioning the same variable (but see the following paragraph).
• The list of variables and their respective domains. For each variable, provide the tuple (var,[value]) giving the variable and the list of all values in its domain. The variables should be distinct.

The function returns the list of solutions. Each solution is a list of bindings.

As an example, the N-Queen problem is susceptible to this algorithm. The variables are the queens, and we use Ints from 1 to N for their variable IDs as well as their home columns (so we just need to solve for their rows). Each variable's domain is the rows, also Ints from 1 to N.

 queen n = forward queenconflict [(q, [1..n]) | q <- [1..n]]

The only source of conflict: if queen q (in column q) chooses row p and queen q' chooses row p', there is a conflict iff they attack each other horizontally or diagonally.

 queenconflict (q,p) (q',p') = p==p' || abs(q-q') == abs(p-p')

There is no need to worry about q and q' being the same; it does not happen.