Problem 1: Write a procedure
shuffle(L1, L2, L3)
which returns true if list L3 is the result combining lists L1 and L2 such that the first element of L3 is the first element of L1, the second element of L3 is the first element of L2, the third element of L3 is the second element of L1, and so on. For example,
shuffle([a,b,c],[d,e,f],[a,d,b,e,c,f])
returns true. You may assume that L1 and L2 have the same number of elements. This should also work:
?- shuffle(X,Y,[1,2,3,4,5,6]).
X = [1, 3, 5], Y = [2, 4, 6].
Problem 2: Write a procedure
double(L1, L2)
which returns true if every element in list L1 appears twice in L2, according to the pattern in the following example:
?- double([a,b,c],[a,a,b,b,c,c]).
true.
Note that the elements that are duplicated are also adjacent to each other.
These cases should also work:
?- double(X,[a,a,b,b,c,c]).
X = [a, b, c].
?- double([a,b,c],X).
X = [a, a, b, b, c, c].
This case should not work:
?- double([a,b,c],[a,b,c,a,b,c]).
false.
Problem 3: Write a procedure
no_duplicates(L1, L2)
which returns true if list L2 is the result of removing all duplicate elements from list L1. For example,
no_duplicates([a,b,c,b,d,b],[a,c,d,b])
returns true (note that the last duplicate ‘b’ is the one that remains). This should also work:
?- no_duplicates([a,b,c,b,d,b],X).
X = [a, c, d, b] ; X = [a, c, d, b] ; false.
Problem 4: Write a procedure
same_elements(L1, L2)
which returns true if lists L1 and L2 contain exactly the same elements, although possibly in different order. For example,
same_elements([a,b,c],[b,c,a]) same_elements([a,b,c],[a,c,b]) same_elements([a,b,c],[c,b,a])
all return true. This should also work:
?- same_elements([a,b,c],X).
X = [a, b, c] ;
X = [b, a, c] ;
X = [b, c, a] ;
X = [a, c, b] ;
X = [c, a, b] ; X = [c, b, a] ; false.
Problem 5: Using the minidoku program that we explored in Episode 26 as inspiration, create a sudoku problem solver that works with any 9×9 grid as described in the slide that defines the sudoku problem. Your solution should be fast when few spaces in the grid are unfilled, but it can be slower (not astronomically slower) when more spaces are unfilled, just like our demonstration in class. You will find the minidoku code in your lecture slides. The skeleton code (i.e., you fill in the missing parts) for the 9×9 solution can be found on Canvas in the Files tab — look for sudokusolver.pl.
Do NOT use the Constraint Logic Programming features provided by SWI-Prolog.
Add your code to the existing skeleton and submit your result via Canvas as a separate file called “sudoku.pl” (not “hw4.pl”).
