Software Paradigms (Lesson 6) Logic Programming Table of Contents 1 Introduction 2 2 Facts 3 3 Predicates



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Software Paradigms


Software Paradigms (Lesson 6)

Logic Programming

Table of Contents

1 Introduction 2

2 Facts 3

3 Predicates (Structured Terms) 4

4 Logical Operations and Composite Predicates 7

5 Rules of Inference 10

6 Some Analogies 17



  1. Introduction


Logic programming offers a formalism for specifying a computation in terms of logical relations between entities. A logic program consists of a collection of logic statements describing a certain state of affairs. In order to write a program in such a language, the programmer must first describe all the relevant logical relationships between the various entities. A computation in essence, amounts to determining whether a particular conclusion follows from those logical statements. The implementation of a logic programming language provides an automatic theorem prove which is used to establish the validity of the conclusion. An important point to writing logic programs is to model the domain (or state of affairs) in terms of logical relations. The relative ease with which this can be done depends on the domain itself. The following section provides some insights on how to represent "ordinary" statements in logic programming.
  1. Facts


A Logical Data Model operates with so-called Facts. Generally speaking, fact is any expression which can be interpreted as True or False.

Examples:



  • Alex is a nice guy.

  • 25 > 36

  • 25 + 41 = 66

  • .Smith is a student and he got “2” for examination on Software Paradigms
  1. Predicates (Structured Terms)


Facts are presented as so-called structured terms that are made of several components.
    1. General Structures


A structured term is syntactically formed by a functor (also called a predicate symbol) and a list of arguments. The list of arguments appears between parentheses. Each argument is separated from the following one by a comma.

Examples:



  • Alex is a nice guy. = = niceGuy (Alex)

Functor argument

  • 25 > 36. = = greater Than (25,36)

Functor arguments

  • 25 + 41 = 66. = = plus (25,41,66)

Functor arguments

  • .Smith is a student and he got “2” for examination on Software Paradigms

= = exam(‘Smith’,’Software Paradigms’,2)

Functor arguments
    1. Predicates (Syntax)


A structured term is syntactically formed by a functor (also called a predicate symbol) and a list of arguments. The list of arguments appears between parentheses. Each argument is separated from the following one by a comma.

Each argument of a predicate is a so-called term, for example, it can be another predicate (structured term). The number of arguments of a structured term is called its arity.

Examples:

greaterThan(9, 6) exam(a, b, h(c,d)) plus(2, 3, 5)

An important point to mention is that a structure is simply a mechanism for combining terms together, thus forming more complex ones but without performing any implicit evaluation. So for instance, in the last example above, the integers 2, 3, and 5 are combined with the functor plus. An equally valid combination, albeit a slightly misleading one, could be plus(2, 3, 7).

If evaluated,



  • plus(2,3,5) is True

  • plus(2,3,7) is False
    1. Simple Terms


The following subsections describe the three different kinds of simple data objects.
    1. Atoms


An atom can be formed in four ways:

  • A letter, which can possibly be followed by other letters (either case), digits, and the underscore character.

Examples:

a greaterThan two_B_or_not_2_b



  • A string of special characters such as: + - * / \ = ^ < > : . ~ @ # $ &

Examples:

<> ##&& ::=

  • A string of any characters enclosed within single quotes.

Examples:

'ABC' '1234' 'a<>b'

In the third case above, if what appears between the single quotes is a valid atom as specified by one of the first two cases, then the quotes are optional. Thus, 'abc' and abc represent the same atom.

    1. Numbers


Applications involving heavy numerical calculations are rarely written in Logic Programming Languages. In fact, Logic Paradigm is not particularly well suited to write such applications. Nevertheless, the paradigm provides representation for integers and real numbers. The following examples illustrate integer representation:

0 -16 33 +100

Real numbers can be written in standard or scientific notation, as illustrated in the following:

0.5 -3.1416 6.23e+23 11.0e-3 -2.6e-2

A decimal point must appear in all real numbers and there must be at least one digit on either side of it. Several points with respect to the representation of numbers are implementation dependent. A brief list of some of these points follows. The actual range of numbers (real and integer) that can be represented.

    1. Variables


A variable is a name for value which can change while an evaluation is running.

Examples:

StudentName may be ‘Johns’, ‘Mayer’ , ‘Ivanov’, etc.

ExaminationMark may be 1, 2, 3, 4 and 5

GoodGuy may be True and False

Set of all possible values which may have a particular variable is called a variable scope or a variable range.

Please note the following (!)

A predicate (structured term) having all arguments as constants is called a fact. A particular Fact can be either true or false.

Example:


  • greaterThan (25,36) is False

  • plus (25,41,66) is True

Why ?

Because we know formal rules to validate the facts and can communicate these rules to computers, i.e. an algorithm can be defined. Such terms are called computational terms.



  • niceGuy(Alex) is True

  • exam(‘Smith’,’Software Paradigms’) is False

Why ?

Because we know that from our own experience which cannot be formalized as an algorithm. We can simply put such knowledge into a computer’s memory by listing all facts which are valid. For example, we can list all guys who considered to be nice or list all students who pass an examination on software paradigms. Such facts are called basic facts or database facts. Formally speaking, a logic programming paradigm heavily rely on a database of persistent data objects which list all basic facts which are valid. If a particular basic fact cannot be found in such database, it is considered to be false.

A predicate (structured term) having at least one variable argument is called a logical function or simply predicate. All variables used for definition of such predicate are called free variables. A particular logical function maps each combination of free variable values onto True/False boolean value. In other words, predicate can be true or false depending on a particular combination of free variable values.

Example:


  • greaterThan (25,X) is a function of variable “X” F(X)

greaterThan(25,X) is True if X = 26, 27, etc. the same predicate is false if X = 25, 24, etc.

  • plus (X,Y,66) is True

plus(X,Y,66) is True if {X,Y} = {1,65}, {2,64}, {3,63}, etc. the same predicate is false if , for example, {X,Y} = {30,45} or {X,Y} = {55,55}.

  • niceGuy(X) is True

niceGuy(X) is True if X = “Alex” and “Alex” can be found among persistent database objects “niceGuy”,

exam(StudentName,VO) is False if StudentName = “Alex”, VO = “Software Paradigm” and (“Alex”,”Software Paradigms” can be found among persistent database objects “exam”),


  1. Logical Operations and Composite Predicates

    1. Logical Operations


Syntactically, a predicate can be composed of one or more simple predicates using logical operation “&” – logical “and”, “|” – logical “or”, “°” – logical “not” and brackets.

Example:


greaterThan(25,36) & plus(55,11,66) is True because

greaterThan(25,36) is True and plus(55,11,66) is True

niceGuy(‘Alex’) | exam(‘Alex’,’Software Paradigms’) is True if one of the facts: niceGuy(‘Alex’) or exam(‘Alex’,’Software Paradigms’) can be found in the database.

The operator precedence for the '&' and '|', operators follow the standard precedence rules, i.e. '&' binds stronger than '|'. Thus,

F1 & F2 | F3 == ( F1 & F2 ) | F3

Explicit use of parenthesis, as in predicate above, is required to override this default precedence. Thus if the intention is for the '|' operator to bind stronger in the above expression, it has to be written as

F1 & (F2 | F3)

Example


  • niceGuy(‘Alex’) & niceGuy(‘Tom’) | exam(‘Tom’, ‘Software Paradigm’,1)

is True if both basic facts: niceGuy(‘Alex’) and niceGuy(‘Tom’) are True or the other fact exam(‘Tom’,’Software Paradigms’,1) is true. In other words, if the fact exam(‘Tom’,’Software Paradigms’,1) is true, it is already sufficient for the whole expression to be valid (true).

  • niceGuy(‘Alex’) & (niceGuy(‘Tom’) | exam(‘Tom’, ‘Software Paradigm’,1))

is True if basic fact: niceGuy(‘Alex’) is true and the composite predicate niceGuy(‘Tom’) and exam(‘Tom’,’Software Paradigms’,1) is true. In other words, if the fact exam(‘Tom’,’Software Paradigms’,1) is true, it is not sufficient for the whole expression to be valid (true).
    1. Quantifiers


Predicates may also include variable quantifiers, specifically:

  • the existential quantifier, denoted by the symbol '', and

  • the universal quantifier, denoted by the symbol ''

These quantifiers quantify variables. An existentially quantified variable, say X, is written "X" and is read as "there exists an X such that...". A universally quantified variable is written as "X" and is read as "for all X...".

Quantification is applied to a formula and is written preceding it. For example,

x (greaterThan(Y,X) & greaterThan(12,Y))

would be read as "there exists an X such that X is less than Y and Y is less than 12". The formula to which the quantification is applied is called the scope of quantification. Occurrences of quantified variables in the scope of quantification are said to be bound (existentially or universally). The scope is normally obvious from the written expressions, but if ambiguities might otherwise arise, we will use parenthesis to delimit scope.

Informally, the formula “X ( )" asserts that there exists at least one value of X (from among its range of values) such that is true. This assertion is false only when no value of X can be found to satisfy . On the other hand, if the assertion is true, there may be more than one such value of X, but we don't care which (!). In other words, the truth of an existentially quantified expression is not a function of the quantified variable(s).

As an example, consider the unquantified expression

greaterThan(Y,X) & greaterThan(12,Y)

and suppose X ranges over {4,15} and Y over {7,14}. The truth table for the expression is:



X

Y

greaterThan(Y,X) & greaterThan(12,Y)

4

7

True

15

7

False

4

14

False

15

14

False

Now consider the same expression but with X existentially quantified:

X (greaterThan(Y,X) & greaterThan(12,Y))

Since we don't care which value of X makes the expression true as long as there is at least one, its truth depends only on the unbound variable Y:

Y

X (greaterThan(Y,X) & greaterThan(12,Y))

7

True

14

False

The truth of a quantified expression does depend, of course, on the range of permitted values of the quantified variables.

Let's turn now to the universal quantifier. Informally, the formula "X ( > )" asserts that for every value of X(from among its range of values) is true. Like the existential quantifier, the truth of an existentially quantified expression is not a function of the quantified variable(s).

Consider, for example, the unquantified expression

greaterThan(Y,X) | greaterThan(12,Y)

and suppose X ranges over {4,15} and Y over {7,14}.

The truth table for the expression is:

X

Y

greaterThan(Y,X) | greaterThan(12,Y)

4

7

True

15

7

True

4

14

True

15

14

False

Now consider the same expression but with X universally quantified:

X (greaterThan(Y,X) & greaterThan(12,Y))

In a sense, like the existentially quantified variable, we don't care what the values of X are, as long as every one of them makes the expression true for any given Y. Thus its truth table is:

:


Y

X (greaterThan(Y,X) | greaterThan(12,Y))

7

True

14

False

Note that the different types of quantifiers can be mixed. But note also that their order is significant, i.e. x y ( ) is not the same as y x ( ).

Suppose, for example, a predicate is_a_mother(M,Ch) defines a set of database facts “ M is the mother of Ch”.

Ch M (is_a_mother(M,Ch))

asserts that everyone has a mother. Whereas,

M Ch (is_a_mother(M,Ch))

asserts that there is a single individual (M) who is the mother of everyone!


    1. Well-Formed Formulae


Let us now be more precise about the valid forms of predicates involving variables. Such valid forms are called well-formed formulae (WFF) and are defined as follows:

1. F(X1, ... Xn) is a WFF where X1, X2, .. are called free variables

2. F1 & F2 and F1 | F2 are WFFs if F1 and F2 are WFFs

3. (F) is a WFF if F is a WFF

4. x (F(X)) and x (F(X)) are WFFs if F(X) is a WFF with a free occurrence of the variable X. In this case, X is called a bound variable in the scope of F(x).

  1. Rules of Inference

    1. Head and Body


The general form of clauses is as follows:

:- .

Note, these two component (body and head) may be also called conclusion and premise respectively.



:-

where


body is a WFF (logical expression)

head is a predicate

If we do not use variables, both components (i.e. head and body) are facts. The rule is interpreted as follows:

“If a body is valid (true) then head is also valid (true) by definition”

Examples:

humanBeing(‘Alex’):-goodGuy(‘Alex’)

if Alex is a good guy, then he is a human being.

Suppose, fact enrollVO(‘Alex’, ‘Programming’) is true if a student “Alex” is enrolled for the course “Programming”.

Suppose also, the fact locVO(‘Programming’, ‘Thursady, ‘Room 22’) is true if the classes on Programming take place in room 22 on Thursday. We can define the following rule of inference

mustGo(‘Alex’, ‘Thursday’,’Room 22’) :-

enrollVO(‘Alex’, ‘Programming’) & locVO(‘Programming’, ‘Thursday’, ‘Room 22’)

meaning that the fact mustGo(‘Alex’, ‘Thursday’, ‘Room 22’) is true if both facts

enrollVO(‘Alex’, ‘Programming’) and locVO(‘Programming’, ‘Thursday’, ‘Room 22’)

are true.

Actually, all the examples above are not very illustrative because rules of inference are almost never defined without variables.

If we use variables, both components (i.e. head and body) are predicates. The rule is interpreted as follows:

“If a body is valid (true) for a particular combination of values of free variables then the head is also valid (true) for the same combination of values of free variables”

Examples:

humanBeing(X):-goodGuy(X)

any (!) good guy is a human being.

Suppose, predicate enrollVO(Student, Subject) is true if a student is enrolled for a course on this particular subject.

Suppose also, the predicate locVO(Subject, Day, Room) is true if classes on this particular subject takes place in this room and on this day.

We can define the following rule of inference

mustGo(Student, Room, Day, Subject) :-

enrollVO(Student, Subject) & locVO(Subject, Day, Room)

meaning that a particular student should be in a particular room on a stipulated day, if he/she enrolled for a classes and this classes take place in this room on that day.

Predicates enrollVO and locVO definitely operates on database facts, i.e. system should search an internal database to find whether, for example, the fact enrollVO(‘Alex’, ‘Programming’) is true or false.

Suppose, the database looks as follows:



locVO

Subject

Day

Room




Hypermedia

Monday

Room 22




Databases

Tuesday

Room 23




Programming

Thursday

Room 22

The table above, just told us that, for example, the fact locVO (Databases,Tuesday,Room 23) is true, at the same time the fact locVO (Databases,Monday,Room 23) is false, etc.

In analogy, basic facts enrollVO may be seen as the following table.



enrollVO

Student

Subject




Palina

Hypermedia




Palina

Programming




Alex

Programming




Alex

Databases

Result of application the following rule of inference

mustGo(Student, Room, Day, Subject) :-

enrollVO(Student, Subject) & locVO(Subject, Day, Room)

to the above mentioned basic facts is a number of inferred facts !

All inferred facts are true by definition and can be seen as the following table:

mustGo

Student

Room

Day

Subject




Palina

Room 22

Monday

Hypermedia




Palina

Room 22

Thursday

Programming




Alex

Room 22

Thursday

Programming




Alex

Room 23

Tuesday

Databases

In other words, the system automatically infer such facts as

mustGo(Palina,Room 22, Monday, Hypermedia) is true:

“Palina should go to Room 22 on Monday to participate in classes on Hypermedia”

mustGo(‘Alex’,’Room 22’, ‘Monday’, ‘Hypermedia’) is false:

“Alex has nothing to do in Room 22 on Monday”

The last example suggest that perhaps we should simplify the rule by removing the Subject variable from the head:

mustGo(Student, Room, Day) :-

enrollVO(Student, Subject) & locVO(Subject, Day, Room)

and inferring the following facts:



mustGo

Student

Room

Day




Palina

Room 22

Monday




Palina

Room 22

Thursday




Alex

Room 22

Thursday




Alex

Room 23

Tuesday

Actually, this rule is erroneous because free variables in the head of the rule are not identical to the variables in the body.

Thus, in order to verify a fact mustGo(‘Alex’,’Room 22’, ‘Thursday’), the system should evaluate the following expression:

enrollVO(‘Alex’, Subject) & locVO(Subject, ‘Room 22’, ‘Thursday’)

Obviously the expression is not a fact and depends on a particular Subject. For example, it is true, if subject is programming and false for any other value.

enrollVO(‘Alex’, ‘Programming’) & locVO(‘Programming’, ‘Room 22’, ‘Thursday’) – true

enrollVO(‘Alex’, ‘Databases’) & locVO(‘Databases’, ‘Room 22’, ‘Thursday’) – false

Following to the formal definition of a rule of inference:

“If a body is valid (true) for a particular combination of values of free variables then the head is also valid (true) for the same combination of values of free variables”

the system simply cannot make conclusion on whether the expression

mustGo(‘Alex’,’Room 22’, ‘Thursday’) is true or false.

There is of course an obvious solution for the problem: existential quantifier.

Thus, we can define the rule as the following:

mustGo(Student, Room, Day) :-

Subject (enrollVO(Student, Subject) & locVO(Subject, Day, Room))

The above predicate (body) is true if there exists at least one subject such that the condition

enrollVO(Student, Subject) & locVO(Subject, Day, Room) is true.

In other words, the fact mustGo(‘Alex’,’Room 22’, ‘Thursday’) is definitely true since there exists a subject, for example, “Programming” such that

enrollVO(‘Alex’, ‘Programming’) & locVO(‘Programming’, ‘Room 22’, ‘Thursday’) is true.


    1. Recursion


Recursive definition of rules of inference is a very powerful and commonly used method of logic programming.

Suppose, we have a predicate

parent(G,D)

the predicate identifies whether a person D is a child of person G.

We can easily define a new predicate descendent(G,D) that identifies whether a person D is a descendent of person G as

descendent(G,D):-parent(G,D) | X (parent(G,X) & descendent(X,D))

Note that rules like this one, can be defined more clearly using a number of rules of inference with one and the same head.

descendent(G,D):-parent(G,D)

descendent(G,D):- X (parent(G,X) & descendent(X,D))


  • first rule for finding direct descendent just copy all facts “parent” as facts “descendent”

  • the second rule uses the same head predicate (i.e., recursion) to find indirect descendent.

Here, if the inferred fact is true in accordance with one of the rules, it is considered to be true generally.

Consider the following genealogy tree,



It may be described as the following facts:



parent

G

D




Nick

Pauline




Pauline

Alex




Pauline

Paul




Alex

Tom

Application of the rule:

descendent(G,D):-parent(G,D)

results in the following inferred facts



descendent

G

D




Nick

Pauline




Pauline

Alex




Pauline

Paul




Alex

Tom

Application of the rule:

descendent(G,D):- X (parent(G,X) & descendent(X,D))

results in the following inferred facts

descendent

G

D

X

parent(G,X) & descendent(X,D))




Nick

Alex

Pauline

parent(‘Nick’,’Pauline’) & descendent(‘Pauline’,’Alex’)




Nick

Paul

Pauline

parent(‘Nick’,’Pauline’) & descendent(‘Pauline’,’Paul’)




Pauline

Tom

Alex

parent(‘Pauline’,’Alex’) & descendent(‘Alex’,’Tom’)




Nick

Tom

Pauline

parent(‘Nick’,’Pauline’) & descendent(‘Pauline’,’Tom’)

    1. Inferred Calculations


In Logic Programming, calculation are performed by means of computational terms looking as, for example, follows:

  • plus(V1,V2,R) the predicate is true if R = V1 + V2;

  • mult(V1,V2,R) the predicate is true if R = V1 * V2;

etc.

Consider, for example, a well-known example of a so-called bill of materials that identify which components and in which amount are needed to assembly a particular product / another component.



It may be described as the following facts: need(P,C,Q)



need

P

C

Q




D_A

D_B

5




D_B

D_D

3




D_B

D_C

8




D_D

D_E

4

The following rules identify how many direct and indirect components are needed to assembly a particular product / component

need+(P,C,Q):-need(P,C,Q)

need+( P,C,Q):- X Q1 Q2(need(P,X,Q1) & need+(X,C,Q2) & mult(Q1,Q2,Q))

Application of the rule:

need+(P,C,Q):-need(P,C,Q)

results in the following inferred facts

Need+

P

C

Q




D_A

D_B

5




D_B

D_D

3




D_B

D_C

8




D_D

D_E

4

Application of the rule:

need+( P,C,Q):- X Q1 Q2(need(P,X,Q1) & need+(X,C,Q2) & mult(Q1,Q2,Q))

results in the following inferred facts

need+

P

C

Q

X,Q1,Q2

need(P,X,Q1) & need+(X,C,Q2) & mult(Q1,Q2,Q)




D_A

D_D

15

D_B, 5, 3

need(D_A,D_B,5) & need+(D_B,D_D,3) & mult(5,3,15)




D_A

D_C

40

D_B, 5, 8

need(D_A,D_B,5) & need+(D_B,D_C,8) & mult(5,8,40)




D_B

D_E

12

D_C, 3, 4

need(D_B,D_C,3) & need+(D_C,D_E,4) & mult(3,4,12)




D_A

D_E

60

D_B, 5, 12

need(D_A,D_B,5) & need+(D_B,D_E,12) & mult(5,12,60)

    1. Functions and Variables


Defining calculations as computational predicates involving multiple bound variable as, for example,

need+( P,C,Q):- X Q1 Q2(need(P,X,Q1) & need+(X,C,Q2) & mult(Q1,Q2,Q))

may constitute a serious problem.

Functions provide much more straight and simple way to define calculations. A particular function may be applied to a number of free variables and be reused further as a variable.

For example, the previously defined rule of inference need+ may be defined in much more clear way as:

need+( P,C,(Q1 * Q2)):- X (need(P,X,Q1) & need+(X,C,Q2) )

where notation (Q1 * Q2) is used in a normal mathematical way

Application of this rule results in the same list of inferred facts, but the inference procedure is much simple



need+

P

C

Q1*Q2

X

need(P,X,Q1) & need+(X,C,Q2)




D_A

D_D

15

D_B

need(D_A,D_B, 5) & need+(D_B,D_D, 3)




D_A

D_C

40

D_B

need(D_A,D_B, 5) & need+(D_B,D_C, 8)




D_B

D_E

12

D_C

need(D_B,D_C,3) & need+(D_C,D_E,4)




D_A

D_E

60

D_B

need(D_A,D_B,5) & need+(D_B,D_E,12)



  1. Some Analogies


One might think of predicates and facts in several ways. One natural way (perhaps especially so for non-programmers) to look at rules of inference is to think of them as declarative sentences in natural language. As stated earlier, predicates describe relationships between entities (e.g., grand-parenthood, parenthood, etc.). Some relationships can be stated in one sentence, other relationships require more than one sentence (rule of inference) in order to specify the relationship fully.

Programmers might find similarities between a predicate and a procedure. In that sense, a predicate specifies how to "compute" something (e.g., finding a ascendant to an individual). In this context, rules would correspond to a procedure call and general directives would correspond to calls involving built-in procedures (this last analogy is weaker than the others). To regard predicates and queries in this way is certainly accurate, however, it constitutes a restricted view.


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