[If you want to have a go straight away, jump to the examples at the bottom of
this post.]
Making a novel logic puzzle has been a bucket list item for me since
yesteryear and I was finally handy enough with Prolog to endeavour for
something elegant without having to write reams of code. I arbitrarily decided
that I wanted the puzzle to be expressed in terms of Dominoe tiles. I tried a
whole bunch of ideas which either resulted in eventual nonsense or puzzles
which were too easy to solve. Eventually, I came up with “Domicles”; a fun and
challenging puzzle, presented herein with examples and a Prolog implementation.
“Domicles” is a combinatorial game using standard (double6) Dominoe tiles. It
is most enjoyable when played with a physical set of tiles. Here is an easy
game:
The tiles are arranged 4×2 making a grid of 4×4 numbers as above. The aim
of the game is to swap the tiles around such that every row and column in the
4×4 number grid has only unique values. The tiles cannot be flipped or rotated;
just moved to different locations in the 4×2 layout.
Let’s solve the first example. The puzzle above has duplicates in grid columns
2 and 4, and grid row 2. Clearly 40 and 10 should not be in the same column,
and neither should 32 and 42. We can resolve both by swapping 10 and 32 to
get the following state:
We now have no column conflicts, but duplicates in rows 1 and 2, which can be
resolved by swapping 21 and 10 to remove all remaining conflicts and solve
the puzzle:
Generating games like these is simple to express in Prolog as a constraint
logic program over integers:
: use_module(library(clpfd)).
domicles([AB,CD,EF,GH,IJ,KL,MN,OP]) :
% All cells range from 06.
[A,B,C,D,E,F,G,H,I,J,K,L,M,N,O,P] ins 0..6,
% Grid rows should have distinct cells.
all_different([A,B,C,D]),
all_different([E,F,G,H]),
all_different([I,J,K,L]),
all_different([M,N,O,P]),
% Grid columns shuold have distinct cells.
all_different([A,E,I,M]),
all_different([B,F,J,N]),
all_different([C,G,K,O]),
all_different([D,H,L,P]),
% Flipping tiles is not allowed.
A#>=B,C#>=D,E#>=F,G#>=H,
I#>=J,K#>=L,M#>=N,O#>=P,
AB#=7*A+B,CD#=7*C+D,EF#=7*E+F,GH#=7*G+H,
IJ#=7*I+J,KL#=7*K+L,MN#=7*M+N,OP#=7*O+P,
% Tiles should not be reused.
all_different([AB,CD,EF,GH,IJ,KL,MN,OP]),
label([A,B,C,D,E,F,G,H,I,J,K,L,M,N,O,P]).
which can be run like this:
? domicles(Solved),random_permutation(Solved,Shuffled).
Solved = [10,32,21,40,42,51,53,64],
Shuffled = [40,32,10,21,64,53,51,42].
Prolog will generate all possible puzzles on backtracking. I’ll leave
randomising the order in which puzzles are generated and avoiding generating
the same puzzle (but with different column/row order) as straightforward
exercises for the reader.
The 4×2 puzzles are easy, mostly because it is possible to resolve conflicts
one at a time without having to think forward. However, to make the puzzle
challenging, I just have to include more tiles. For example, here is a 4×3
layout:
The randomised version above can be unreasonably hard to solve because swapping
tiles is likely to cause other conflicts, and you therefore have to think
several moves ahead to avoid getting stuck.
The Prolog implementation of the 4×3 layouts is a direct extension of the
simpler version, so I won’t dwell on it here. To make the difficulty
configurable, I generate a solved puzzle and then perform N swaps at random
(whilst making sure no swap accidentally solves the puzzle), so that the
maximum distance to a solution is known:
swap(L,AB,X) :
nth1(A,L,V1),
nth1(B,L,V2),
replace(L,A,V2,X0),
replace(X0,B,V1,X).
replace(L,I,V,X) :
nth1(I,L,_,R),
nth1(I,X,V,R).
shuffle(0,L,L) :!.
shuffle(N0,A0,L) :
N is N01,
findall(XY,(between(1,12,X),between(1,12,Y),Y>X),All0),
random_permutation(All0,All),
member(P,All),
swap(A0,P,A),
+ puzzle(A),!,
shuffle(N,A,L).
Here it is applied to the puzzle above so that it can be solved in at most 3
swaps:
? domicles(Solved),shuffle(3,Solved,Shuffled).
Solved = [10,32,54,21,40,65,52,64,30,63,51,42]
Shuffled = [21,32,54,40,10,65,52,64,30,51,63,42]
If you’ve tried to do it you may have noted that the puzzle is still
challenging but now solvable. I’ve observed that the difficulty of any Domicles
puzzle seems related to the layout and the number of swaps used in the
shuffle. The analysis below offers some insight into why that is the case.
If I take as a standard that MxN layouts are rectangular with (Mge N)
((M) are rows), then the possible number of Dominoes are either 1 or a multiple
of 2. Double tiles cannot be used since they always violate the uniqueness
rule, therefore the maximum number of tiles in a layout is 20 (e.g. 5×4).
The number of puzzle tile sets for a 4×2 layout, calculated by enumeration, is
6704. It is 2085 for a 4×3 layout. Meanwhile, the number of puzzles calculated
by enumeration is 842,832 and 448,416 respectively. Of these,
(6704times 4!times 2! = 321792) and (2085times 4!times 3! = 403920) are
accounted for by variants of the intended solution. The total number of
possible tile sets are (C(21,8)=203490) and (C(21,12)=293930) respectively.
Many interesting things can be observed and deduced from these facts:

The difference between the layouts is just the addition of 1 row. I suspect
that therefore the main effect is constraints on the columns, not the rows. 
There are more than 3 times fewer puzzle tile sets for the 4×3 puzzle,
and only (2085/293930=0.007) of possible tile sets constitute a 4×3 puzzle
compared to (6704/203490=0.033) in the 4×2 case. 
Corollary of 2, generally 4×2 puzzles cannot be “upgraded” to 4×3 puzzles
by adding tiles since there are more of them. This has possible implications
on whether puzzles can be solved by “divide and conquer” methods. 
Tile sets at both sizes have more solutions (after variants are accounted
for) than tile sets. In the 4×2 case, the number of puzzles imply
(841832/4!2!=17538.17) tile sets but actually there are only 6704. Therefore,
on average, there are (17538.17/6704 = 2.62) solutions per tile set. In the
4×3 case the puzzles imply (448415/4!3! = 3114) tile sets but there are
actually 2085, hence on average, there are (3114/2085 = 1.49) solutions per
tile set. 
Any given 4×3 tile set will have (12!) possible arrangements of which
(1.49times 4!3!=215) will be solutions on average, compared to (8!) possible
arrangements and (2.62times 4!2!=126) solutions on average in the 4×2 case.
That is, in the 4×3 layout there are (4.5times 10^{7}) solutions per
arrangement, compared to (3.1times 10^{3}): a difference of 3 orders of
magnitude. 
From 5, if I were to define a distance matrix between arrangements of a
particular tile set in terms of the number of tile swaps required to turn one
arrangement into another, on average, the distances between arrangements in
the 4×3 case would be bigger than that of the 4×2 case since – on average –
there are more arrangements in between any given pair. I would expect these
distances to be proportional to the number of arrangements, and therefore I
expect the 4×3 layout to be exponentially more complicated than the 4×2 layout.
I’ve presented a configurable, fun and challenging logic puzzle which can be
played with standard Dominoe tiles and implemented with a handful of Prolog. It
required significantly more effort than this presentation may suggest to
conceive the idea, but I learned a lot about how to construct combinatorial
puzzles in the process. I’ve also provided a quick partial analysis with
regards to the size and complexity of the puzzle, mostly because I needed to
convince myself that the puzzles really do get harder as the number of tiles
increase. Finally, I’ve left a few additional examples for you to try. Best
enjoyed with some physical Dominoe tiles!
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