This document discusses Prolog programming. It covers data structures in Prolog like lists and terms, programming techniques like guess-and-verify queries and open lists, and control in Prolog through goal ordering and rule selection. Lists can represent data structures and terms through partial specification with variables. Programming with open lists and difference lists allows modification of data.

1. Prolog Programming

2. 2
Prolog ProgrammingProlog Programming
 DATA STRUCTURES IN PROLOG
 PROGRAMMING TECHNIQUES
 CONTROL IN PROLOG
 CUTS

3. 3
DATA STRUCTURES IN
PROLOG
 Lists in Prolog
List notation is a way of writing terms
 Terms as Data
Term correspond with list

4. 4
Lists in Prolog
 The simplest way of writing a list is to
enumerate its elements.
The list consisting of the 3 atoms a, b and c can be
written as
[a, b, c]
The list that doesn’t have elements called empty
list denoted as [ ]

5. 5
Lists in Prolog
 We can also specify an initial sequence of
elements and a trailing list, separated by |
The list [a, b, c] can also be written as
[a, b, c | [ ] ]
[a, b | [c] ]
[a | [b, c] ]

6. 6
Lists : Head & Tail
 A special case of this notation is a list with
head H and tail T, written as [H|T]
 The head is the first element of a list, and
 The tail is the list consisting of the remaining
elements.
The list [a, b, c] can also be separated as
• Head:The first element is a
• Tail:The list of remaining elements = [b, c]

7. 7
Lists : Unification
 Unification can be used to extract the
components of a list, so explicit operators for
extracting the head and tail are not needed.
The solution of the query
 Bind variable H to the head and variable T to
the tail of list [a, b, c].
?- [H | T] = [a, b, c].
H = a
T = [b, c]

8. 8
Lists : Specified terms
 The query (partially specified terms)
 The term [ a | T ] is a partial specification of a
list with head a and unknown tail denoted by
variable T.
 Similarly, [ H, b, c] is a partial specification of a
list with unknown head H and tail [b, c].
 These two specification to unify H = a, T =[b,c]
?- [a | T] = [H, b, c].
T = [b, c]
H = a

9. 9
Lists in Prolog
 Example 2 The append relation on lists is
defined by the following rules:
Append([ ], Y, Y).
Append([H | X], Y, [H | Z]) :- append(X,Y,Z).
In words,
The result of appending the empty list [ ] and a list Y is Y.
If the result of appending X and Y is Z, then
the result of appending [H | X] and Y is [H | Z]

10. 10
Lists : Compute Arguments
 The rules for append can be used to compute
any one of the arguments from the other two:
 Inconsistent arguments are rejected
?- append([a, b], [c, d], Z).
Z = [a, b, c, d]
?- append([a, b], Y, [a, b, c, d]).
Y = [c, d]
?- append(X, [c, d], [a, b, c, d]).
X = [a, b]
?- append(X, [d, c], [a, b, c, d]).
no

11. 11
Terms as Data
 The Dot operator or functor ‘.’ corresponds to
make list with H and T.
 [H | T ] is syntactic sugar for the term .(H,T)
 Lists are terms. The term for the list [a, b, c] is
.(H,T)
.(a, .(b, .(c, [])))

12. 12
Terms as Data
 following terms can be drawn a tree
 There is a one-to-one correspondence
between trees and terms
.(a, .(b, .(c, [])))
∙
∙
∙
a
b
c []

13. 13
Terms : Binary Tree
 Binary trees can be written as terms
 An atom leaf for a leaf
 A functor nonleaf with 2 arguments
leaf
nonleaf(leaf,leaf)
nonleaf(nonleaf(leaf,leaf), nonleaf(leaf,leaf))
nonleaf(nonleaf(leaf,leaf),leaf)
nonleaf(leaf,nonleaf(leaf,leaf))

14. 14
List : tree
 Example 3 A binary search tree is either
empty, or it consists of a node with two binary
search trees as subtrees.
 Each node holds an integer.
 Smaller elements appear in the left subtree of
a node and larger elements appear in the right
subtree.
 Let a term node(K,S,T) represent a tree
K
S T

15. 15
Binary search trees
15
2 16
10
129
0
3
19
10
2 12
9
153
0 16
3

16. 16
Binary search trees
 The rules define a relation member to test
whether an integer appear at some node in a
tree. The two arguments of member are an
integer and a tree.
member(K,_,_).
member(K, node(N,S,_)) :- K < N, member(K, S).
member(K, node(N,_,T)) :- K > N, member(K, T).

17. 17
PROGRAMMING TECHNIQUES
 The strengths of Prolog namely, backtracking
and unification.
 Backtracking allows a solution to be found if
one exists
 Unification allows variables to be used as
placeholders for data to be filled in later.
 Careful use of the techniques in this section
can lead to efficient programs. The programs
rely on left-to-right evaluation of subgoals.

18. 18
Guess and Verify
 A guess-and-verify query has the form
Where guess(S) and verify(S) are subgoals.
 Prolog respond to a query by generating
solutions to guess(S) until a solution satisfying
verify(S) is found. Such queries are also called
generate-and-test queries.
Is there an S such that
guess(S) and verify(S)?

19. 19
Guess and Verify
 Similarly, a guess-and-verify rule has the
following form:
 Example
Conslusion(…) if guess(…,S,…) and verify(…,S,…)
overlap(X, Y) :- member(M, X), member(M, Y).
Two lists X and Y overlap if there is some M that is a
member of both X and Y. The first goal member(M, X)
guesses an M from list X, and the second goal member(M,
Y) verifies that M also appears in list Y.

20. 20
 The rules for member are
member(M, [M |_]).
Member(M, [_ |T]) :- member(M, T).
The first rule says that M is a member of a list with head
M. The second rule says that M is a member of a list if M
is a member of its tail T.

21. 21
Consider query
 These query
 The first goal in this query generates
solutions and the second goal tests to see
whether they are acceptable.
?- overlap([a,b,c,d],[1,2,c,d]).
yes
?- member(M,[a,b,c,d]),member(M,[1,2,c,d]).

22. 22
Consider query
 The solutions generated by the first goal are
 Test the second goal
?- member(M,[a,b,c,d]).
M = a;
M = b;
M = c;
M = d;
no
?- member(a,[1,2,c,d]).
no
?- member(b,[1,2,c,d]).
no
?- member(c,[1,2,c,d]).
yes

23. 23
Hint
 Since computation in Prolog proceeds from
left to right, the order of the subgoals in a
guess-and-verify query can affect efficiency.
 Choose the subgoal with fewer solutions as
the guess goal.
 Example of the effect of goal order
?- X = [1,2,3], member(a,X).
no
?- member(a,X), X = [1,2,3]).
[infinite computation]

24. 24
Variables as Placeholders in Terms
 Variables have been used in rules and
queries but not in terms representing objects.
 Terms containing varibales can be used to
simulate modifiable data structures;
 The variables serve as placeholders for
subterms to be filled in later.

25. 25
Represent Binary Trees in Terms
 The terms leaf and nonleaf(leaf,leaf)
are completely specified.
leaf
nonleaf(leaf,leaf)

26. 26
Partially specified list
 The example list [a, b | X] has
 Its first element : a
 Its second element : b
 Do not yet know what X represents
 “Open list” if its ending in a variable, referred
“end marker variable”
 “Close list” if it is not open.

27. 27
How prolog know variable
 Prolog used machine-generated variables,
written with a leading underscore (“_”)
followed by an integer.
?- L = [a, b | X].
L = [a, |_G172]
X = _G172
Yes

28. 28
 Prolog generates fresh variables each time it
responds to a query or applies a rule.
 An open list can be modified by unifying its
end marker
?- L = [a, b | X], X = [c,Y].
L = [a,b,c |_G236]
X = [c,_G236]
Y = _G236
Yes

29. 29
 Extending an open list by unifying its end
marker.
a b
L X
_172
a b
L X
_236
c
(a) Before X is bound. (b) After X = [c | Y].

30. 30
 Unification of an end-marker variable is akin
to an assignment to that variable.
 List L changes from
[a, b | _172]  [a, b, c | _236]
when _172 unifies with [c | _236]
 Advantage of working with open lists is that
the end of a list can be accessed quickly.

31. 31
Open list implement queues
when a queue is created, where L is an open list with
end marker E
When element a enters queue Q, we get queue R.
When element a leaves queue Q, we get queue R.
q(L,E)
enter(a,Q,R)
leave(a,Q,R)

32. 32
Open list implement queue
?- setup(Q).
?- setup(Q), enter(a,Q,R).
?- setup(Q), enter(a,Q,R), leave(S,R,T).
?- setup(Q), enter(a,Q,R), enter(b,R,S),
leave(X,S,T),leave(Y,T,U), wrapup(q([],[])).
setup(q(X,X)).
enter(A, q(X,Y), q(X,Z)) :- Y = [A | Z].
leave(A, q(X,Z), q(Y,Z)) :- Y = [A | Y].
wrapup(q([],[])).

33. 33
Test queue
?-setup(Q),enter(a,Q,R),enter(b,R,S),leave(X,S,T),
leave(Y,T,U),wrapup(U).
Q = q([a, b], [a, b])
R = q([a, b], [b])
S = q([a, b], [])
X = a
T = q([b], [])
Y = b
U = q([], [])
Yes
?-

34. 34
Operations on a queue
Q
_1
R
_2
a
a
T
_3
b
Q
Q R
setup(Q)
enter(a,Q,R)
enter(b,R,S)

35. 35
Operations on a queue
a
T
_3
b
X
leave(X,S,T)
a
T
_3
b
Y
leave(Y,T,U)

36. 36
Internal Prolog
 A queue q(L,E) consists of open list L with
end marker E.
 The arrows from Q therefore go to the empty
open list _1 with end marker _1.
setup(q(X,X)).
?-setup(Q).
Q = q(_1,_1)
yes

37. 37
Second goal
 To enter A into a queue q(X,Y),
bind Y to a list [A|Z],
where Z is a fresh end marker,
and return q(X,Z).
enter(A,q(X,Y),q(X,Z)):- Y = [A|Z].
?-setup(Q),enter(a,Q,R).
Q = q([a|_2], [a|_2])
R = q([a|_2], _2)
Unifies _1 with [a|_2],where _2 is a fresh end marker

38. 38
 When an element leaves a queue q(L,E), the
resulting queue has the tail of L in place of L.
Note in the diagram to the right of
leave(X,S,T) that the open list for queue T is
the tail of the open list for S.
 The final goal wrapup(U) checks that the
enter and leave operations leave U in an
initial state q(L,E), where L is an empty
openlist with end marker E.

39. 39
Difference Lists
 Difference List are a technique for coping with
such changes.
 Difference List consists of a list and its suffix.
 We write this difference list as
dl(L,E).

40. 40
Contents of Difference List
 The contents of the difference list consist of
the elements that are in L but not in E.
 Examples of difference lists with contents
[a,b] are
dl([a,b],[]).
Dl([a,b,c],[c]).
Dl([a,b|E],E).
Dl([a,b,c|F],[c|F]).

41. 41
CONTROL IN PROLOG
 In the informal equation
 “Logic” refers to the rules and queries in a
logic program and
 “control” refers to how a language computes
a response to a query.
algorithm = logic + control

42. 42
CONTROL IN PROLOG
 Control in Prolog is characterized by two
decisions
 Goal order : Choose the leftmost subgoal.
 Rule order : Select the first applicable rule.
 The response to a query is affected both by
goal order within the query and by rule order
with in the database of facts and rules.

43. 43
CONTROL IN PROLOG
start with a query as the current goal;
while the current goal is nonempty do
choose the leftmost subgoal;
if a rule applies to the subgoal then
select the first applicable rule;
form a new current goal
else
backtrack
end if
end while;
succeed

44. 44
Example
 A sublist S of Z can be specified in the
following seemingly equivalent ways:
 preffix X of Z and suffix S of X.
 suffix S of X and prefix X of Z.
appen1([],Y,Y).
appen1([H|X],Y,[H|Z]):- appen1(X,Y,Z).
Prefix(X,Z) :- appen1(X,Y,Z).
Suffix(Y,Z) :- appen1(X,Y,Z).
appen2([H|X],Y,[H|Z]):- appen2(X,Y,Z).
appen2([],Y,Y).

45. 45
Queries
 The corresponding queries usually produce
the same responses.
 Rule order can also make a difference.
?-prefix(X,[a,b,c]),suffix([e],X).
no
?-suffix([e],X),prefix(X,[a,b,c]).
[infinite computation]

46. 46
Queries
?- appen1(X,[c],Z).
X = []
Z = [c] ;
X = [_G230]
Z = [_G230, c] ;
X = [_G230, _G236]
Z = [_G230, _G236, c] ;
?- appen2(X,[c],Z).
 New Solutions are produced on demand for

47. 47
Unification an Substitutions
 Unification is central to control in Prolog
 Substitution is a function from variables to
terms

48. 48
Applying a Rule to a Goal
 A rule applies to a
subgoal G if its head A unifies with G
 Variables in the rule are renamed before
unification to keep them distinct from
variables in the subgoal.
A :- B1, B2, …, Bn

49. 49
A computation that succeeds without backtracking
GOAL
Suffix([a],L),prefix(L,[a,b,c]).
suffix([a],L) if append(_1,[a],L).
Append(_1,[a],L),prefix(L,[a,b,c]).
{_1[],L[a]} append([],[a],[a]).
Prefix([a],[a,b,c]).
prefix([a],[a,b,c]) if append([a],_2,[a,b,c])
append([a],_2,[a,b,c]).
prefix([a],[a,b,c]) if append([],_2,[b,c])
Append([],_2,[b,c]).
{_2[b,c]} append([],[b,c],[b,c])
yes

50. 50
Prolog Search Trees

51. 51
Goal Order Changes Solutions

52. 52
Cuts
 A cut prunes or “cuts out” and unexplored
part of a Prolog search tree.
 Cuts can therefore be used to make a
computation more efficient by eliminating
futile searching and backtracking.
 Cuts can also be used to implement a form of
negation

53. 53
Cuts
 A cut, written as !, appears as a condition
within a rule. When rule
is applied, the cut tells control to backtrack
past Cj-1,…,C1,B, without considering any
remaining rules for them.
B :- C1,…, Cj-1, !,Cj+1,…,Ck

54. 54
A cut as the First Condition
 Consider rules of the form
 If the goal C fails, then control backtracks
past B without considering any remaining
rules for B. Thus the cut has the effect of
making B fail if C fails.
B :- !, C.

55. 55
Example
b :- c.
b :- d.
b :- e.
b,G
c,G e,G
X
d,G!,d,G
d,G
b :- c.
b :- !,d.
b :- e.

56. 56
Example
 ?-a(X).
a(1) :- b;
a(2) :- e;
b :- c.
b :- d.
a(1) :- b;
a(2) :- e;
b :- !,c.
b :- d.
a(X)
b e
c d Yes
X=2Yes
X=1
backtrack
a(X)
b e
!c d Yes
X=2
backtrack
c

57. 57
The Effect of Cut
 As mentioned earlier, when a rule
is applied during a computation
 The cut tells control to backtrack past Cj-
1,..C1,B without considering any remaining
rules for them.
 The effect of inserting a cut in the middle of a
guess-and-verify rule.
B :- C1,…, Cj-1, !,Cj+1,…,Ck

58. 58
The Effect of Cut
 The right side of a guess-and-verify rule has
the form guess(S), verify(S), where guess(S)
generates potential solutions until one
satisfying verify(S) is found.
 The effect of insering a cut between them, as
is to eliminate all but the first guess.
Conclusion(S) :- guess(S), !, verify(S)

59. 59
a(X) :- b(X).
a(X) :- f(X).
b(X) :- g(X),v(X).
b(X) :- X = 4, v(X).
g(1).
g(2).
g(3).
v(X).
f(5)
a(X) :- b(X).
a(X) :- f(X).
b(X) :- g(X),!,v(X).
b(X) :- X = 4, v(X).
g(1).
g(2).
g(3).
v(X).
f(5)
(a) (b)

60. 60
a(Z)
b(Z) f(5)
g(Z),v(Z) v(4)
v(1) v(2) v(3)
a(Z)
b(Z) f(5)
g(Z),!,v(Z) v(4)
!v(X)
v(1)
v(2) v(3)
(a) (b)

61. 61
Negation as Failure
 The not operator in Prolog is implemented by
the rules
not(X) :- X, !, fail.
not(_).