Maximize distance between variables' value in SWI-Prolog(clpfd) - prolog

I want to maximize the difference between two variables:
:- use_module(library(clpfd)).
maximize(X) :-
X = [A,B],
X ins 1..5,
% I want to write a constraint to have maximum difference between A and B.


There is no constraint to express maximum difference. Nor any constraint to express a maximum1. To permit such a construct some form of quantification would be necessary. And/or problems with monotonicity would arise.
However, there is something related: you might want to generate values such that the largest value is produced first:
:- use_module(library(clpfd)).
maximize(Xs) :-
Xs = [A,B],
Xs ins 1..5,
labeling([max(abs(A-B))],Xs).
?- maximize(Xs).
Xs = [1,5]
; Xs = [5,1]
; Xs = [1,4]
; Xs = [2,5]
; Xs = [4,1]
; ... .
So it starts with the biggest distance and then lowers it one-by one.
At first this behavior might surprise you, but recall what the manual states
Labeling is always complete, always terminates, and yields no redundant solutions.
This is a very strong guarantee!
If you now want just the first solution, wrap a once/1 around labeling/2 but keep in mind that you then left the realm of pure, monotonic programs.
Footnote 1: It is possible to express the maximum of two variables max(X,Y) but this does not constrain the maximum to be the largest possible value! It will be only the largest value of X and Y.

Related

CLPFD ins operator yields not sufficiently instantiated error

So, my goal is to make a map colourer in Prolog. Here's the map I'm using:
And this are my colouring constraints:
colouring([A,B,C,D,E,F]) :-
maplist( #\=(A), [B,C,D,E] ),
maplist( #\=(B), [C,D,F]),
C #\= D,
maplist( #\=(D), [E,F]),
E #\= F.
Where [A,B,C,D,E,F] is a list of numbers(colors) from 1 to n.
So I want my solver to, given a List of 6 colors and a natural number N, determine the colors and N constraints both ways, in a way that even the most general query could yield results:
regions_ncolors(L,N) :- colouring(L), L ins 1..N, label(L).
Where the most general query is regions_ncolors(L,N).
However, the operator ins doesn't seem to accept a variable N, it instead yields an argument not sufficiently instantiated error. I've tried using this solution instead:
int_cset_(N,Acc,Acc) :- N #= 0.
int_cset_(N,Acc,Cs) :- N_1 #= N-1, int_cset_(N_1,[N|Acc],Cs).
int_cset(N,Cs) :- int_cset_(N,[],Cs).
% most general solver
regions_ncolors(L,N) :- colouring(L), int_cset(N,Cs), subset(L,Cs), label(L).
Where the arguments in int_cset(N,Cs) is a natural number(N) and the counting set Sn = {1,2,...,N}
But this solution is buggy as regions_ncolors(L,N). only returns the same(one) solution for all N, and when I try to add a constraint to N, it goes in an infinite loop.
So what can I do to make the most general query work both ways(for not-instantiated variables)?
Thanks in advance!
Btw, I added a swi-prolog tag in my last post although it was removed by moderation. I don't know if this question is specific to swi-prolog which is why I'm keeping the tag, just in case :)

Your colouring is too specific, you encode the topology of your map into it. Not a problem as is, but it defeats of the purpose of then having a "most general query" solution for just any list.
If you want to avoid the problem of having a free variable instead of a list, you could first instantiate the list with length/2. Compare:
?- L ins 1..3.
ERROR: Arguments are not sufficiently instantiated
ERROR: In:
ERROR: [16] throw(error(instantiation_error,_86828))
ERROR: [10] clpfd:(_86858 ins 1..3) ...
Is that the same problem as you see?
If you first make a list and a corresponding set:
?- length(L, N), L ins 1..N.
L = [],
N = 0 ;
L = [1],
N = 1 ;
L = [_A, _B],
N = 2,
_A in 1..2,
_B in 1..2 ;
L = [_A, _B, _C],
N = 3,
_A in 1..3,
_B in 1..3,
_C in 1..3 .
If you use length/2 like this you will enumerate the possible lists and integer sets completely outside of the CLP(FD) labeling. You can then add more constraints on the variables on the list and if necessary, use labeling.
Does that help you get any further with your problem? I am not sure how it helps for the colouring problem. You would need a different representation of the map topology so that you don't have to manually define it within a predicate like your colouring/1 you have in your question.

There are several issues in your program.
subset/2 is impure
SWI's (by default) built-in predicate subset/2 is not the pure relation you are hoping for. Instead, it expects that both arguments are already sufficiently instantiated. And if not, it takes a guess and sticks to it:
?- colouring(L), subset(L,[1,2,3,4,5]).
L = [1,2,3,4,2,1].
?- colouring(L), subset(L,[1,2,3,4,5]), L = [2|_].
false.
?- L = [2|_], colouring(L), subset(L,[1,2,3,4,5]), L = [2|_].
L = [2,1,3,4,1,2].
With a pure definition it is impossible that adding a further goal as L = [2|_] in the third query makes a failing query succeed.
In general it is a good idea to not interfere with labeling/2 except for the order of variables and the options argument. The internal implementation is often much faster than manual instantiations.
Also, your map is far too simple to expose subset/2s weakness. Not sure what the minimal failing graph is, but here is one such example from
R. Janczewski et al. The smallest hard-to-color graph for algorithm DSATUR, Discrete Mathematics 236 (2001) p.164.
colouring_m13([K1,K2,K3,K6,K5,K7,K4]):-
maplist(#\=(K1), [K2,K3,K4,K7]),
maplist(#\=(K2), [K3,K5,K6]),
maplist(#\=(K3), [K4,K5]),
maplist(#\=(K4), [K5,K7]),
maplist(#\=(K5), [K6,K7]),
maplist(#\=(K6), [K7]).
?- colouring_m13(L), subset(L,[1,2,3,4]).
false. % incomplete
?- L = [3|_], colouring_m13(L), subset(L,[1,2,3,4]).
L = [3,1,2,2,3,1,4].
int_cset/2 never terminates
... (except for some error cases like int_cset(non_integer, _).). As an example consider:
?- int_cset(1,Cs).
Cs = [1]
; loops.
And don't get fooled by the fact that an actual solution was found! It still does not terminate.
#Luis: But how come? I'm getting baffled by this, the same thing is happening on ...
To see this, you need the notion of a failure-slice which helps to identify the responsible part in your program. With some falsework consisting of goals false the responsible part is exposed.
All unnecessary parts have been removed by false. What remains has to be changed somehow.
int_cset_(N,Acc,Acc) :- false, N #= 0.
int_cset_(N,Acc,Cs) :- N1 #= N-1, int_cset_(N1,[N|Acc],Cs), false.
int_cset(N,Cs) :- int_cset_(N,[],Cs), false.
?- int_cset(1, Cs), false.
loops.
Adding the redundant goal N1 #> 0
will avoid unnecessary non-termination.
This alone will not solve your problem since if N is not given, you will still encounter non-termination due to the following failure slice:
regions_ncolors(L,N) :-
colouring(L),
int_cset(N,Cs), false,
subset(L,Cs),
label(L).
In int_cset(N,Cs), Cs occurs for the first time and thus cannot influence termination (there is another reason too, its definition would ignore it as well..) and therefore only N has a chance to induce termination.
The actual solution has been already suggested by #TA_intern using length/2 which liberates one of such mode-infested chores.

Why doesn't this clpfd query terminate until I add a redundant constraint?

I've written some predicates which take the length of a list and attaches some constraints to it (is this the right vocabulary to be using?):
clp_length([], 0).
clp_length([_Head|Rest], Length) :-
Length #>= 0, Length #= Length1 + 1,
clp_length(Rest, Length1).
clp_length2([], 0).
clp_length2([_Head|Rest], Length) :-
Length #= Length1 + 1,
clp_length2(Rest, Length1).
The first terminates on this simple query, but the second doesn't:
?- Small in 1..2, clp_length(Little, Small).
Small = 1,
Little = [_1348] ;
Small = 2,
Little = [_1348, _2174] ;
false.
?- Small in 1..2, clp_length2(Little, Small).
Small = 1,
Little = [_1346] ;
Small = 2,
Little = [_1346, _2046] ;
% OOPS %
This is strange to me, because Length is pretty clearly greater than 0. To figure that out you could either search, find the zero, and deduce that adding from zero can only increase the number, or you could propagate the in 1..2 constraint down. It feels like the extra clause is redundant! That it isn't means my mental model of clpfd is pretty wrong.
So I think I have two questions (would appreciate answers to the second as comments)
Specifically, why does this additional constraint cause the query to work correctly?
Generally, is there a resource I can use to learn about how clpfd is implemented, instead of just seeing some examples of how it can be used? I'd prefer not to have to read Markus Triska's thesis but that's the only source I can find. Is that my only option if I want to be able to answer questions like this one?

1mo, there is the issue with naming. Please refer to previous answers by
mat
and me recommending relational names. You won't go far using inappropriate names. So list_length/2 or list_fdlength/2 would be an appropriate name. Thus we have list_fdlength/2 and list_fdlength2/2.
2do, consider the rule of list_fdlength2/2. Nothing suggests that 0 is of relevance to you. So that rule will be exactly the same if you are using 0 or 1 or -1 or whatever as base case. So how should this poor rule ever realize that 0 is the end to you? Better, consider a generalization:
list_fdlength2(fake(N), N) :- % Extension to permit fake lists
N #< 0.
list_fdlength2([], 0).
list_fdlength2([_Head|Rest], Length) :-
Length #= Length1 + 1,
list_fdlength2(Rest, Length1).
This generalization shows all real answers plus fake answers. Note that I have not changed the rule, I added this alternative fact only. Thus the fake solutions are actually caused by the rule:
?- list_fdlength2(L, 1).
L = [_A]
; L = [_A, _B|fake(-1)]
; L = [_A, _B, _C|fake(-2)]
; ... .
?- list_fdlength2(L, 0).
L = []
; L = [_A|fake(-1)]
; L = [_A, _B|fake(-2)]
; ... .
Each clause tries to contribute to the solutions just in the scope of the clause. But there is no way to derive (by the built-in Prolog execution mechanism) that some rules are no longer of relevance. You have to state that explicitly with redundant constraints as you did.
Now, back to your original solution containing the redundant constraint Length #>= 0. There should not be any such fake solution at all.
list_fdlength(fake(N), N) :-
N #< 0.
list_fdlength([], 0).
list_fdlength([_Head|Rest], Length) :-
Length #>= 0,
Length #= Length1 + 1,
list_fdlength(Rest, Length1).
?- list_fdlength(L, 1).
L = [_A]
; L = [_A, _B|fake(-1)] % totally unexpected
; false.
?- list_fdlength(L, 0).
L = []
; L = [_A|fake(-1)] % eek
; false.
There are fake answers, too! How ugly! At least, they are finite in number. But, you could have done it better by using
Length #>= 1 in place of Length #>=0. With this little change, there are no longer any fake solutions when N is non-negative and thus also your original program will be better.

Prolog - partially ordered set

There is a partially ordered set relation le(X,Y), when Y mod X = 0
(so there are le(1,5), le(5,70), le(7,14) etc.)
I have to make predicates
max(X) is X maximum element
greatest(X) is X the greatest element
defining max(X) is simple, because
max(X) :- \+ le(X,A), le(B,X). (there isn't any greater element and X is in set)
But how about greatest(X)?

For the least upper bound (LUB), you need two sets. First the argument set S, that you are asking for the LUB, and then the partial order T where you are searching for the LUB. So input is as follows:
T the partial order
S the set, S subset T
The code is then very similar as for the max. Just use range restricted formulas, that search over the partial order. This works in ordinary Prolog for finite partial orders.
Here is your divisibility example:
?- [user].
ls(X,Y) :-
Y mod X =:= 0.
bound(M,Y) :-
\+ (member(X,M),
\+ls(X,Y)).
lub(S,T,Y) :-
member(Y,T), bound(S,Y),
\+ (member(Z,T), bound(S,Z),
\+ls(Y,Z)).
^D
And here are some example runs:
?- lub([3,2],[1,2,3,4,5,6,7,8,9,10],Y).
Y = 6 ;
false.
?- lub([5,3],[1,2,3,4,5,6,7,8,9,10],Y).
false.
?- lub([5,3],[1,2,3,4,5,6,7,8,9,10,11,12,14,15,16,17,18,19,20],Y).
Y = 15 ;
false.
The above very general algorithm is not the efficientest, it is of order m^2*n^2, where n is the size of S and m is the size of T. For infinite partial orders you would need to invent something with CLP(X).

How do I freeze a goal for a list of variables?

My ultimate goal is to make a reified version of automaton/3, that freezes if there are any variables in the sequence passed to it. i.e. I dont want the automaton to instantiate variables.
(fd_length/3, if_/3 etc as defined by other people here on so).
To start with I have a reified test for single variables:
var_t(X,T):-
var(X) ->
T=true;
T=false.
This allows me to implement:
if_var_freeze(X,Goal):-
if_(var_t(X),freeze(X,Goal),Goal).
So I can do something like:
?-X=bob,Goal =format("hello ~w\n",[X]),if_var_freeze(X,Goal).
Which will behave the same as:
?-Goal =format("hello ~w\n",[X]),if_var_freeze(X,Goal),X=bob.
How do I expand this to work on a list of variables so that Goal is only called once, when all the vars have been instantiated?
In this method if I have more than one variable I can get this behaviour which I don't want:
?-List=[X,Y],Goal = format("hello, ~w and ~w\n",List),
if_var_freeze(X,Goal),
if_var_freeze(Y,Goal),X=bob.
hello, bob and _G3322
List = [bob, Y],
X = bob,
Goal = format("hello, ~w and ~w\n", [bob, Y]),
freeze(Y, format("hello, ~w and ~w\n", [bob, Y])).
I have tried:
freeze_list(List,Goal):-
freeze_list_h(List,Goal,FrozenList),
call(FrozenList).
freeze_list_h([X],Goal,freeze(X,Goal)).
freeze_list_h(List,Goal,freeze(H,Frozen)):-
List=[H|T],
freeze_list_h(T,Goal,Frozen).
Which works like:
?- X=bob,freeze_list([X,Y,Z],format("Hello ~w, ~w and ~w\n",[X,Y,Z])),Y=fred.
X = bob,
Y = fred,
freeze(Z, format("Hello ~w, ~w and ~w\n", [bob, fred, Z])) .
?- X=bob,freeze_list([X,Y,Z],format("Hello ~w, ~w and ~w\n",[X,Y,Z])),Y=fred,Z=sue.
Hello bob, fred and sue
X = bob,
Y = fred,
Z = sue .
Which seems okay, but I am having trouble applying it to automaton/3.
To reiterate the aim is to make a reified version of automaton/3, that freezes if there are any variables in the sequence passed to it. i.e. I don't want the automaton to instantiate variables.
This is what I have:
ga(Seq,G) :-
G=automaton(Seq, [source(a),sink(c)],
[arc(a,0,a), arc(a,1,b),
arc(b,0,a), arc(b,1,c),
arc(c,0,c), arc(c,1,c)]).
max_seq_automaton_t(Max,Seq,A,T):-
Max #>=L,
fd_length(Seq,L),
maplist(var_t,Seq,Var_T_List), %find var_t for each member of seq
maplist(=(false),Var_T_List), %check that all are false i.e no uninstaninated vars
call(A),!,
T=true.
max_seq_automaton_t(Max,Seq,A,T):-
Max #>=L,
fd_length(Seq,L),
maplist(var_t,Seq,Var_T_List), %find var_t for each member of seq
maplist(=(false),Var_T_List), %check that all are false i.e no uninstaninated vars
\+call(A),!,
T=false.
max_seq_automaton_t(Max,Seq,A,true):-
Max #>=L,
fd_length(Seq,L),
maplist(var_t,Seq,Var_T_List), %find var_t for each
memberd_t(true,Var_T_List,true), %at least one var
freeze_list_h(Seq,A,FrozenList),
call(FrozenList),
call(A).
max_seq_automaton_t(Max,Seq,A,false):-
Max #>=L,
fd_length(Seq,L),
maplist(var_t,Seq,Var_T_List), %find var_t for each
memberd_t(true,Var_T_List,true), %at least one var
freeze_list_h(Seq,A,FrozenList),
call(FrozenList),
\+call(A).
Which does not work, The following goal should be frozen until X is instantiated:
?- Seq=[X,1],ga(Seq,A),max_seq_automaton_t(3,Seq,A,T).
Seq = [1, 1],
X = 1,
A = automaton([1, 1], [source(a), sink(c)], [arc(a, 0, a), arc(a, 1, b), arc(b, 0, a), arc(b, 1, c), arc(c, 0, c), arc(c, 1, c)]),
T = true
Update This is what I now have which I think works as I originally intended but I am digesting what #Mat has said to think if this is actually what I want. Will update further tomorrow.
goals_to_conj([G|Gs],Conj) :-
goals_to_conj_(Gs,G,Conj).
goals_to_conj_([],G,nonvar(G)).
goals_to_conj_([G|Gs],G0,(nonvar(G0),Conj)) :-
goals_to_conj_(Gs,G,Conj).
max_seq_automaton_t(Max,Seq,A,T):-
Max #>=L,
fd_length(Seq,L),
maplist(var_t,Seq,Var_T_List), %find var_t for each member of seq
maplist(=(false),Var_T_List), %check that all are false i.e no uninstaninated vars
call(A),!,
T=true.
max_seq_automaton_t(Max,Seq,A,T):-
Max #>=L,
fd_length(Seq,L),
maplist(var_t,Seq,Var_T_List), %find var_t for each member of seq
maplist(=(false),Var_T_List), %check that all are false i.e no uninstaninated vars
\+call(A),!,
T=false.
max_seq_automaton_t(Max,Seq,A,T):-
Max #>=L,
fd_length(Seq,L),
maplist(var_t,Seq,Var_T_List), %find var_t for each
memberd_t(true,Var_T_List,true), %at least one var
goals_to_conj(Seq,GoalForWhen),
when(GoalForWhen,(A,T=true)).
max_seq_automaton_t(Max,Seq,A,T):-
Max #>=L,
fd_length(Seq,L),
maplist(var_t,Seq,Var_T_List), %find var_t for each
memberd_t(true,Var_T_List,true), %at least one var
goals_to_conj(Seq,GoalForWhen),
when(GoalForWhen,(\+A,T=false)).

In my view, you are making great progress with Prolog. At this point it makes sense to proceed a bit more prudently though. All the things you are asking for can, in principle, be solved easily. You only need a generalization of freeze/2, which is available as when/2.
However, let us take a step back and more deeply consider what is actually going on here.
Declaratively, when we state a constraint, we mean that it holds. We do not mean "It holds only when everything is instantiated", because that would reduce the constraint to a mere checker, leading to a "generate-and-test" approach. The point of constraints is exactly to prune whenever possible, leading to a much reduced search space in many cases.
Exactly the same holds for reified constraints. When we post a reified constraint, we state that the reification holds. Not only in cases where everything is instantiated, but always. The point is exactly that the (reified) constraint can be used in all directions. If the constraint that is being reified is already entailed, we get to know it. Likewise, if it cannot hold, we get to know it. If either possibility may be the case, we need to search explicitly for solutions, or determine that none exist. If we want to insist that the constraint that is being reified holds, it is easily possible; etc.
However, the point in all cases is exactly that we can focus on the declarative semantics of the constraint, very free from extra-logical, procedural considerations like what is being instantiated and when. If I answered your literal question, it would move you closer to operational considerations, much closer than you probably need or want in actuality.
Therefore, I am not going to answer your literal question. But I will give you a solution to your actual, underlying issue.
The point is to reifiy automaton/3. A constraint reification will not by itself prune anything as long as it is open whether the constraint that is being reified actually holds or not. Only when we insist that the constraint that is being reified holds does propagation occur.
It is easy to reify automaton/3, by reifying the conjunction of constraints that constitute its decomposition. Here is one way to do it, based on code that is freely available in SWI-Prolog:
:- use_module(library(clpfd)).
automaton(Vs, Ns, As, T) :-
must_be(list(list), [Vs,Ns,As]),
include_args1(source, Ns, Sources),
include_args1(sink, Ns, Sinks),
phrase((arcs_relation(As, Relation),
nodes_nums(Sinks, SinkNums0),
nodes_nums(Sources, SourceNums0)), [[]-0], _),
phrase(transitions(Vs, Start, End), Tuples),
list_to_drep(SinkNums0, SinkDrep),
list_to_drep(SourceNums0, SourceDrep),
( Start in SourceDrep #/\
End in SinkDrep #/\
tuples_in(Tuples, Relation)) #<==> T.
include_args1(Goal, Ls0, As) :-
include(Goal, Ls0, Ls),
maplist(arg(1), Ls, As).
list_to_drep([L|Ls], Drep) :-
foldl(drep_, Ls, L, Drep).
drep_(L, D0, D0\/L).
transitions([], S, S) --> [].
transitions([Sig|Sigs], S0, S) --> [[S0,Sig,S1]],
transitions(Sigs, S1, S).
nodes_nums([], []) --> [].
nodes_nums([Node|Nodes], [Num|Nums]) -->
node_num(Node, Num),
nodes_nums(Nodes, Nums).
arcs_relation([], []) --> [].
arcs_relation([arc(S0,L,S1)|As], [[From,L,To]|Rs]) -->
node_num(S0, From),
node_num(S1, To),
arcs_relation(As, Rs).
node_num(Node, Num), [Nodes-C] --> [Nodes0-C0],
{ ( member(N-I, Nodes0), N == Node ->
Num = I, C = C0, Nodes = Nodes0
; Num = C0, C is C0 + 1, Nodes = [Node-C0|Nodes0]
) }.
sink(sink(_)).
source(source(_)).
Note that this propagates nothing whatsoever as long as T is unknown.
I now use the following definition for a few sample queries:
seq(Seq, T) :-
automaton(Seq, [source(a),sink(c)],
[arc(a,0,a), arc(a,1,b),
arc(b,0,a), arc(b,1,c),
arc(c,0,c), arc(c,1,c)], T).
Examples:
?- seq([X,1], T).
Result (omitted): Constraints are posted, nothing is propagated.
Next example:
?- seq([X,1], T), X = 3.
X = 3,
T = 0.
Clearly, the reified automaton/3 constraint does not hold in this case. However, the reifying constraint of course still holds, as always, and this is the reason why T=0 in this case.
Next example:
?- seq([1,1], T), indomain(T).
T = 0 ;
T = 1.
Oh-oh! What is going on here? How can it be that the constraint is both true and false? This is because we do not see all constraints that are actually posted in this example. Use call_residue_vars/2 to see the whole truth.
In fact, try it on the simpler example:
?- call_residue_vars(seq([1,1],0), Vs).
The pending residual constraints that still need to be satisfied in this case are:
_G1496 in 0..1,
_G1502#/\_G1496#<==>_G1511,
tuples_in([[_G1505,1,_G1514]], [[0,0,0],[0,1,1],[1,0,0],[1,1,2],[2,0,2], [2,1,2]])#<==>_G825,
tuples_in([[_G831,1,_G827]], [[0,0,0],[0,1,1],[1,0,0],[1,1,2],[2,0,2],[2,1,2]])#<==>_G826,
_G829 in 0#<==>_G830,
_G830 in 0..1,
_G830#/\_G828#<==>_G831,
_G828 in 0..1,
_G827 in 2#<==>_G828,
_G829 in 0..1,
_G829#/\_G826#<==>0,
_G826 in 0..1,
_G825 in 0..1
So, the above only holds if these constraints, which are said to still flounder, also hold.
Here is an auxiliary definition that helps you label remaining finite domain variables. It suffices for this example:
finite(V) :-
fd_dom(V, L..U),
dif(L, inf),
dif(U, sup).
We can now paste back the residual program (which consists of CLP(FD) constraints), and use label_fixpoint/1 to label variables whose domain is finite:
?- Vs0 = [_G1496, _G1499, _G1502, _G1505, _G1508, _G1511, _G1514, _G1517, _G1520, _G1523, _G1526],
_G1496 in 0..1,
_G1502#/\_G1496#<==>_G1511,
tuples_in([[_G1505,1,_G1514]], [[0,0,0],[0,1,1],[1,0,0],[1,1,2],[2,0,2], [2,1,2]])#<==>_G825,
tuples_in([[_G831,1,_G827]], [[0,0,0],[0,1,1],[1,0,0],[1,1,2],[2,0,2],[2,1,2]])#<==>_G826,
_G829 in 0#<==>_G830, _G830 in 0..1,
_G830#/\_G828#<==>_G831, _G828 in 0..1,
_G827 in 2#<==>_G828, _G829 in 0..1,
_G829#/\_G826#<==>0, _G826 in 0..1, _G825 in 0..1,
include(finite, Vs0, Vs),
label(Vs).
Note that we cannot directly use labeling in the original program, i.e., we cannot do:
?- call_residue_vars(seq([1,1],0), Vs), <label subset of Vs>.
because call_residue_vars/2 also brings internal variables to the surface that, although they have a domain assigned and look like regular CLP(FD) variables, are not meant to directly participate in any labeling.
In contrast, the residual program can be used without any problem for further reasoning, and it is in fact meant to be used that way.
In this concrete case, after labeling the variables whose domains are still finite in the case above, some constraints still remain. They are of the form:
tuples_in([[_G1487,1,_G1496]], [[0,0,0],[0,1,1],[1,0,0],[1,1,2],[2,0,2],[2,1,2]])#<==>_G1518
Exercise: Does it follow from this, however indirectly, that the original query, i.e., seq([1,1],0), cannot hold?
So, to summarize:
Constraint reification does not in itself cause propagation of the constraint that is being reified.
Constraint reification often lets you detect that a constraint cannot hold.
In general, CLP(FD) propagation is necessarily incomplete, i.e., we cannot be sure that there is a solution just because our query succeeds.
labeling/2 lets you see whether there are concrete solutions, if domains are finite.
To see all pending constraints, wrap your query in call_residue_vars/2.
As long as pending constraints remain, it is only a conditional answer.
Recommendation: To make sure that no floundering constraints remain, wrap your query in call_residue_vars/2 and look for any residual constraints on the toplevel.

Consider using the widely available prolog-coroutining predicate when/2 (for details, consider reading the SICStus Prolog manual page on when/2).
Note that you can, in principle, implement freeze/2 like this:
freeze(V,Goal) :-
when(nonvar(V),Goal).

What you are implementing appears to me a variation of the following:
delayed_until_ground_t(Goal,T) :-
( ground(Goal)
-> ( call(Goal)
-> T = true
; T = false
)
; T = true, when(ground(Goal),once(Goal))
; T = false, when(ground(Goal), \+(Goal))
).
Delaying goals can be a really nice feature, but be aware of the perils of delaying forever.
Make sure to read and digest the above answer by #mat regarding call_residue_vars/2!

prolog function returning memory locations instead of values

just started programming with prolog and I'm having a few issues. The function I have is supposed to take a value X and copy it N number of times into M. My function returns a list of N number of memory locations. Here's the code, any ideas?
duple(N,_,M):- length(M,Q), N is Q.
duple(N,X,M):- append(X,M,Q), duple(N,X,Q).

Those are not memory adresses. Those are free variables. What you see is their internal names in your prolog system of choice. Then, as #chac pointed out (+1 btw), the third clause is not really making sense! Maybe you can try to tell us what you meant so that we can bring light about how to do it correctly.
I'm going to give you two implementations of your predicate to try to show you correct Prolog syntax:
duple1(N, X, L) :-
length(L, N),
maplist(=(X), L).
Here, in your duple1/3 predicate, we tell prolog that the length of the resulting list L is N, and then we tell it that each element of L should be unified with X for the predicate to hold.
Another to do that would be to build the resulting list "manually" through recursion:
duple2(0, _X, []).
duple2(N, X, [X|L]) :-
N > 0,
NewN is N - 1,
duple1(NewN, X, L).
Though, note that because we use >/2, is and -/2, ie arithmetic, we prevent prolog from using this predicate in several ways, such as:
?- duple1(X, Y, [xyz, xyz]).
X = 2,
Y = xyz.
This worked before, in our first predicate!
Hope this was of some help.

I suppose you call your predicate, for instance, in this way:
?- duple(3,xyz,L).
and you get
L = [_G289, _G292, _G295] ;
ERROR: Out of global stack
If you try
?- length(X,Y).
X = [],
Y = 0 ;
X = [_G299],
Y = 1 ;
X = [_G299, _G302],
Y = 2 ;
X = [_G299, _G302, _G305],
Y = 3 ;
X = [_G299, _G302, _G305, _G308],
Y = 4 .
...
you can see what's happening:
your query will match the specified *M*, displaying a list of M uninstantiated variables (memory locations), then continue backtracking and generating evee longer lists 'til there is stack space. Your second rule will never fire (and I don't really understand its purpose).
A generator is easier to write in this way:
duple(N,X,M) :- findall(X,between(1,N,_),M).
test:
?- duple(3,xyz,L).
L = [xyz, xyz, xyz].

Resources