How to create arithmetic and disequality constraints in Prolog

Question

I'm brand new to Prolog, and I'm interested in converting the following word problem into (SWI) Prolog:

There are 4 children: Abe, Dan, Mary, and Sue. Their ages, in no particular order, are 3, 5, 6, and 8. Abe is older than Dan. Sue is younger than Mary. Sue's age is Dan's age plus 3 years. Mary is older than Abe.

So far I've come up with

child(X) :-
    member(X, [3,5,6,8]).

solution(Abe, Dan, Mary, Sue) :-
    child(Abe),
    child(Dan),
    child(Mary),
    child(Sue),
    Abe > Dan,
    Sue < Mary,
    Sue == Dan+3,
    Mary > Abe,
    Abe \== Dan,
    Abe \== Mary,
    Abe \== Sue,
    Dan \== Mary,
    Dan \== Sue,
    Mary \== Sue.

But running the query

?- solution(Abe, Dan, Mary, Sue)

I just get false. As a side question, will Prolog perform brute force search for solutions, or is there some machinery which can solve this (sort of) problem in better than O(n!) ?

The result I want is Abe = 5, Dan = 3, Mary = 9, Sue = 6.

Answer 1

Arithmetic constraints over integers, such as the constraints in this puzzle, are best expressed with your Prolog system's CLP(FD) constraints.

For example, in SICStus Prolog, YAP or SWI:

:- use_module(library(clpfd)).

ages(As) :-
        As = [Abe,Dan,Mary,Sue],    % There are 4 children: Abe, Dan, Mary, Sue
        As ins 3\/5\/6\/8,          % Their ages are 3, 5, 6 and 8
        all_different(As),
        Abe #> Dan,                 % Abe is older than Dan
        Sue #< Mary,                % Sue is younger than Mary
        Sue #= Dan + 3,             % Sue's age is Dan's age plus 3 years
        Mary #> Abe.                % Mary is older than Abe

Sample query and its result:

?- ages([Abe,Dan,Mary,Sue]).
Abe = 5,
Dan = 3,
Mary = 8,
Sue = 6.

We see from this answer that the puzzle has a unique solution.

Note that no search whatsoever was necessary to obtain this answer! The constraint solver has deduced the unique solution by a powerful implicit mechanism called constraint propagation, which is the key advantage of CLP systems over brute force search. Constraint propagation is successfully used in this example to prune all but one remaining branch of the search tree.

Answer 2

The answer by @WillemVanOnsem—generate and test with low-level arithmetics—is old-school:

• Legacy Prolog code puts the burden of getting all low-level procedural details right on you.

• Debugging / optimizing legacy code often boils down to some variant of program tracing.

In comparison, @mat's code wins on generality / versatility / robustness, declarativity, conciseness, efficiency, and more! How come? Luck? Genius? Divine intervention? Likely a bit of each, but the main reason is this: @mat uses superior tools. @mat uses clpfd.

Good news! clpfd is generally available. Use it & reap the benefits:

• Note how close @mat's Prolog code and the original specs are!

• The code preserves logical-purity. This has important consequences:

  • High-level debugging methods (which harness useful algebraic properties of pure Prolog code) can be used!

  • Low-level debugging is also possible, but explore high-level methods first!