Summary Chapter 6

Sebagian besar bahasa pemrograman mempunyai fasilitas untuk melakukan perulangan/looping, namun tidak begitu dengan prolog. Sebagai pegganti dari looping, user dapat menggunakan backtracking, rekursi, built-in predikat, atau kombinasi dari ketiganya untuk mendapat efek yang sama dengan looping.

6.1 Looping a Fixed Number of Times
Banyak bahasa pemrograman menyediakan ‘untuk loop’ yang memungkinkan satu set instruksi akan dieksekusi tetap beberapa kali. Tidak ada fasilitas tersebut tersedia dalam Prolog (secara langsung), tetapi efek yang sama dapat diperoleh dengan menggunakan rekursi.

Contoh:

loop(0).
loop(N):-N>0,write(‘The value is: ‘),write(N),nl,
M is N-1,loop(M).

6.2 Looping Until a Condition Is Satisfied

Banyak bahasa pemrograman memiliki kondisi ’until loop’ yang memungkinkan sebuah set instruksi yang akan dieksekusi berulang kali sampai kondisi tertentu terpenuhi. Namun di prolog kondisi itu tidak ada dan untuk memperoleh efek yang sama dengan menggunakan:

a. Rekursi.

b. Repeat predicat

6.3 Backtracking with Failure

Prolog backtracking berfungsi untuk pencarian melalui database untuk menemukan semua klausa dengan properti tertentu. Berikut adalah fungsi Prolog Backtracking secara lebih rinci:

6.3.1 Searching the Prolog Database

6.3.2 Finding Multiple Solutions

Chapter Summary

This chapter describes how a set of goals can be evaluated repeatedly in Prolog,

either a fixed number of times or until a specified condition is met, and how

multiple solutions can be arrived at using the technique of 'backtracking with

failure'.

INPUT AND OUTPUT IN PROLOG

1. Tetapkan predikat untuk output nilai kuadrat dari N1 sampai N2 dimana N1=6 dan N2=12.

Langkah-langkahnya adalah sebagai berikut :

a. Buatlah rule seperti dibawah ini di notepad

b. Simpan file tersebut dengan format.pl

c. Buka aplikasi program SWI prolog,kemudian pilih menu file pilih consult.

d. Pilih file yang di simpan sebelumnya.

e. Pada SWI prolog,ketik kata “outsquare(N1,N2)” dan harus di akhiri dengan tanda titik.

f. Kemudian tekan Enter,maka akan muncul tampilan seperti ini :

g. Selesai

2. Menetapkan dan menguji predikat dalam rangkaian huruf dari inputan user dan menampilkan semua inputan tersebut sebelum barisan baru yang pertama atau huruf.

Langkah-langkahnya adalah sebagai berikut :

a. Buatlah rule seperti dibawah ini di notepad

b. Simpan file tersebut dengan format.pl

c. Buka file yang telah di simpan tersebut,maka otomatis akan dibuka pada aplikasi SWI Prolog.

d. Ketik kata “go” yang harus di akhiri dengan tanda titik.

e. Ketiklah kata-kata yang anda inginkan,maka akan muncul tampilan seperti ini

f. Selesai.

2. 3. Gunakan klausa person seperti yang diberikan pada Section 6.3.1,temukan profesi dari semua itu yang umurnya di atas 40.

Langkah-langkahnya adalah sebagai berikut :

a. Buatlah rule seperti dibawah ini di notepad

b. Simpan file tersebut dengan format.pl

c. Buka file yang telah di simpan tersebut,maka otomatis akan dibuka pada aplikasi SWI Prolog.

d. Ketik kata “find” yang harus di akhiri dengan tanda titik.

e. Tekan Enter,maka akan muncul tampilan seperti berikut :

f. Karena yang kita cari hanya profesi orang yang berumur diatas 40 maka selasai sudah programnya.

SUMMARY CHAPTER 5

Pendahuluan

Prolog memiliki fasilitas untuk mengaktifkan input dan output baik dari istilah atau karakter.
Menggunakan istilah lebih sederhana dan akan dijelaskan terlebih dahulu. Awalnya, maka akan diasumsikan bahwa
semua output ke layar pengguna dan semua input adalah dari pengguna keyboard. Masukan dan
output menggunakan file eksternal, e.g. pada hard disk atau CD-ROM, akan dijelaskan dalam
Bagian 5,7 dan seterusnya. Perhatikan bahwa, seperti banyak lainnya predikat built-in, mereka untuk masukan
dan output yang dijelaskan dalam bab ini adalah semua unresatisfiable, yaitu mereka selalu gagal
ketika kemunduran.

5,1 Syarat keluaran

predikat built-in utama yang disediakan untuk istilah output write/ 1, yang di gunakan dalam resume ini

write/ 1 predikat mengambil satu argumen, yang harus yang valid dengan syarat prolog. Evaluasi predikat menyebabkan syarat akan ditulis ke current output stream, , yang secara default adalah layar user.

Built-in predikat nl / 0 juga telah digunakan berkali-kali sebelumnya dalam hal ini

buku. Tanpa membutuhkan argumen. Mengevaluasi sebuah tujuan nl menyebabkan baris baru untuk menjadi output untuk

output stream.

Contoh

? – write(26), nl.

26

ya

? – write( ’string karakter’), nl.

string karakter

ya

? – write([a, b, c, d, [x, y, z]]), nl.

[a, b, c, d, [x, y, z]]

ya

? – write (mypred (a, b, c)), nl.

mypred (a, b, c)

ya

? – write( ‘Contoh useran nl’), nl, nl, write( ‘akhir contoh’), nl.

Contoh useran nl

contoh akhir

ya

Perhatikan bahwa atom yang harus dikutip pada input (misalnya ‘Paulus’, ‘hello world’) tidak

dikutip ketika output menggunakan menulis. Jika penting untuk output tanda kutip, yang

writeq / 1 predikat dapat digunakan. Hal ini identik dengan write/ 1, kecuali bahwa atom yang

memerlukan tanda kutip untuk input adalah output antara tanda kutip (atom lain tidak).

? – writeq ( ’string karakter’), nl.

’string karakter’

ya

?-writeq (anjing), nl.

anjing

ya

? – writeq ( ‘anjing’), nl.

anjing

ya

5,2 Syarat Masukan

Built-in predikat read/ 1 disediakan untuk memasukkan istilah. Dibutuhkan satu argumen,

yang harus menjadi variabel.

Mengevaluasi itu menyebabkan istilah berikutnya untuk dibaca dari input stream,

yang secara default adalah user keyboard.

Dalam input stream, istilah harus diikuti oleh sebuah titik (‘.’) dan setidaknya satu

spasi putih, seperti spasi atau baris baru. Titik dan spasi karakter dibaca dalam tetapi tidak dianggap bagian dari istilah.

Perhatikan bahwa untuk input dari keyboard (hanya) sebuah prompt karakter seperti titik dua

biasanya akan ditampilkan untuk menunjukkan bahwa input user diperlukan. Mungkin perlu

untuk tekan tombol ‘kembali’ tombol sebelum Prolog akan menerima input. Kedua tidak

berlaku untuk input dari file. Ketika sebuah tujuan membaca dievaluasi, istilah input disatukan dengan argumen variabel. Jika variabel tidak terikat (yang biasanya terjadi) itu adalah terikat pada

input nilai.

? – Read (X).

: Jim.

X = jim

? – Read (X).

: 26.

X = 26

? – Read (X).

: Mypred (a, b, c).

X = mypred (a, b, c)

? – Read (Z).

: [A, b, mypred (p, q, r), [z, y, x]].

Z = [a, b, mypred (p, q, r), [z, y, x]]

? – Read (Y).

: ‘String karakter’.

Y = ’string karakter’

Jika variabel argumen sudah terikat , tujuan berhasil jika input istilah identik dengan nilai terikat sebelumnya.

? – X = fred, read (X).

: Jim.

tidak

? – X = fred, read (X).

: Fred.

X = fred

5,3 INPUT dan OUTPUT MENGGUNAKAN KARAKTER

Meskipun input dan output sangat mudah,tapi useran tanda kutip dan titik dapat menjadi rumit dan tidak selalu sesuai. Sebuah pendekatan yang lebih baik untuk masalah semacam ini adalah untuk input sebuah karakter pada satu waktu. Untuk melakukan hal ini, pertama-tama perlu

untuk mengetahui tentang nilai ASCII karakter.

Semua mencetak karakter dan banyak karakter non-cetak (seperti ruang dan

tab) memiliki sesuai ASCII (American Standard Code for Information

Interchange) nilai, yang merupakan integer 0-255. Tabel di bawah ini memberikan nilai ASCII numerik yang sesuai

9	Tab
10	End of record
32	Space
33	!
34	“
35	#
36	$
37	%

38	&
39	‘
40	(
41	)
42	*
43	+
44	,
45	-

46	.
48-57	0-9
58	:
59	;
60	<
61	=
62	>
63	?
64	@
65-90	A to Z

91	[
92	\
93	]
94	^
95	_
96	`
97-122	a to z
123	{
124	|
125	}
126	~

`

5,4 Karakter Keluaran

Karakter adalah output dengan menggunakan built-in predikat meletakkan / 1. Predikat mengambil

argumen tunggal, yang harus menjadi nomor 0-255 atau ekspresi yang

mengevaluasi ke integer dalam jangkauan.

Mengevaluasi tujuan put menyebabkan satu karakter untuk menjadi output untuk saat ini

output stream. Ini adalah karakter yang sesuai dengan nilai numerik (ASCII

nilai) dari argumen, misalnya

?- put(97),nl.

a

yes

?- put(122),nl.

z

yes

?- put(64),nl.

@

Yes

5,5 Karakter Masukan

Dua predikat built-in disediakan untuk memasukkan satu karakter: get0 / 1 da n get/ 1.

Get0 predikat yang mengambil satu argumen, yang harus menjadi variabel. Mengevaluasi

tujuan get0 menyebabkan karakter untuk dibaca dari input saat ini stream. Variabel

kemudian disatukan dengan nilai ASCII karakter ini.

Mengasumsikan argumen variabel tak terikat (yang biasanya akan terjadi), itu

terikat ke nilai ASCII karakter input.

? – Get0 (N).

: A

N = 97

? – Get0 (N).

: Z

N = 90

Logika Pemrograman Dengan 74 Prolog

? – Get0 (M)

)

M = 41

Jika variabel argumen sudah terikat, tujuan berhasil jika dan hanya jika memiliki

nilai numerik yang sama dengan nilai ASCII karakter input.

?- get0(X).

: a

X = 97

?- M is 41,get0(M).

: )

M = 41

?- M=dog,get0(M).

: )

no

?- M=41.001,get0(M).

: )

No

predikat mengambil satu argumen, yang harus menjadi variabel. Mengevaluasi

get berikutnya menyebabkan tujuan non-white-space karakter (yaitu ASCII karakter dengan

nilai kurang dari atau sama dengan 32) untuk dibaca dari input saat ini stream. Itu

variabel ini kemudian disatukan dengan nilai ASCII karakter ini dengan cara yang sama seperti

untuk get0.

?- get(X).

: Z

X = 90

?- get(M).

: Z

M = 90

5,6 Contoh Menggunakan Karakter

Contoh pertama menunjukkan bagaimana membaca dalam serangkaian karakter dari keyboard

finishing dengan * dan untuk output nilai-nilai ASCII yang berhubungan satu per baris

predikat readin didefinisikan secara rekursif. Ini menyebabkan satu karakter untuk

input dan variabel X untuk terikat kepada para (numerik) nilai ASCII. Tindakan diambil

(proses (X) tujuan) tergantung pada apakah atau tidak X memiliki nilai 42 berarti a *

karakter. Jika memiliki, evaluasi tujuan berhenti. Jika tidak, nilai dari X adalah output,

diikuti oleh baris baru, diikuti dengan sebuah panggilan ke readin lebih lanjut. Proses ini berlangsung

tanpa batas waktu sampai a * karakter yang dibaca. (Pada contoh di bawah ini, nilai-nilai ASCII

karakter P, r, o dll benar ditunjukkan untuk menjadi sebesar 80, 114, 111 dll)

?- readin.

: Prolog Example*

80

114

111

108

111

103

32

69

120

97

109

112

108

101

Yes

Contoh berikut adalah versi yang diperluas di atas. Kali ini ASCII

nilai-nilai input adalah karakter yang tidak output, tetapi jumlah karakter

(termasuk *) adalah output. Predikat hitungan didefinisikan dengan dua argumen

yang dapat dibaca sebagai ‘jumlah karakter dihitung sejauh ini’ dan ‘jumlah total

karakter sebelum * ‘.

go(Total):-count(0,Total).

count(Oldcount,Result):-

get0(X),process(X,Oldcount,Result).

process(42,Oldcount,Oldcount).

process(X,Oldcount,Result):-

X=\=42,New is Oldcount+1,count(New,Result).

Contoh terakhir adalah program rekursif, yang didasarkan pada dua sebelumnya, yang

menunjukkan bagaimana membaca dalam serangkaian diakhiri dengan karakter * dan menghitung jumlah

vokal. Karakter dibaca dalam satu demi satu sampai sebuah karakter dengan nilai ASCII 42

(menandakan *) adalah dijumpai.

Di sini, dua argumen dari predikat hitungan dapat diartikan sebagai “

jumlah vokal sejauh ini ‘dan’ jumlah total vokal ‘. Tiga argumen

proses predikat dapat dibaca sebagai “nilai ASCII karakter input ‘,’ yang

jumlah vokal sampai dengan tetapi tidak termasuk karakter ‘dan’ jumlah total

vokal ‘, masing-masing.

Pertama dua argumen dari predikat processChar dapat ditafsirkan dalam

cara yang sama seperti untuk proses, tetapi argumen ketiga adalah “jumlah vokal hingga dan

termasuk karakter (argumen pertama) ‘.

Predikat vokal tes untuk salah satu dari 10 kemungkinan vokal (lima huruf dan

lima huruf kecil), menggunakan nilai-nilai ASCII.

go(Vowels):-count(0,Vowels).

count(Oldvowels,Totvowels):-

get0(X),process(X,Oldvowels,Totvowels).

process(42,Oldvowels,Oldvowels).

process(X,Oldvowels,Totalvowels):-

X=\=42,processChar(X,Oldvowels,New),

count(New,Totalvowels).

processChar(X,Oldvowels,New):-vowel(X),

New is Oldvowels+1.

processChar(X,Oldvowels,Oldvowels).

vowel(65). /* A */

vowel(69). /* E */

vowel(73). /* I */

vowel(79). /* O */

vowel(85). /* U */

vowel(97). /* a */

vowel(101). /* e */

vowel(105). /* i */

Input and Output 77

vowel(111). /* o */

vowel(117). /* u */

?- go(Vowels).

: In the beginning was the word*

Vowels = 8

?- go(Vowels).

: pqrst*

Vowels = 0

5,7 Masukan dan Keluaran menggunakan File

Prolog mengambil semua input dari input stream dan menulis semua output ke

output stream. Secara default kedua stream ini bernama user,

menunjukkan user terminal, yaitu untuk input keyboard dan layar untuk memperoleh output.

Fasilitas yang sama yang tersedia untuk input dan output dari dan ke user

terminal kedua istilah tersebut dengan istilah atau karakter demi karakter juga tersedia untuk input

dan output dari dan ke file (misalnya file pada hard disk atau CD-ROM).

User dapat membuka dan menutup streaminput dan output yang terkait dengan

jumlah nama file, tapi hanya ada satu streaminput dan satu

stream output pada setiap saat. Perhatikan bahwa tidak ada file bisa terbuka untuk input maupun

output pada waktu yang sama (kecuali user) dan bahwa user input dan output stream

tidak dapat ditutup.

5,8 Keluaran: Mengubah Current Output Stream

Stream output dapat diubah menggunakan tell / 1 predikat. Ini membutuhkan

argumen tunggal, yang merupakan atom atau variabel yang mewakili nama file, misalnya

kirim ( ‘outfile.txt’).

Mengevaluasi sebuah tujuan kirim menyebabkan file bernama untuk menjadi arus output

arus. Jika file belum terbuka, file dengan nama tertentu pertama kali diciptakan

(semua file yang sudah ada dengan nama yang sama akan dihapus).

Perhatikan bahwa file yang sesuai dengan stream output sebelumnya tetap

terbuka ketika arus output baru stream dipilih. Hanya stream output

dapat ditutup (menggunakan predikat kata yang dijelaskan di bawah).

Arus output default stream user, yaitu user terminal. Nilai ini dapat

dikembalikan baik dengan menggunakan kata predikat atau dengan kirim (user).

Built-in predikat tell/ 0 mengambil tanpa argumen. Mengevaluasi sebuah tujuan kepada penyebab

arus output file yang akan ditutup dan arus output stream untuk diatur ulang ke user,

i.e. user.

Built-in predikat tell / 1 memerlukan satu argumen, yang harus menjadi variabel

dan biasanya akan terikat. Mengevaluasi sebuah tujuan memberitahu menyebabkan variabel yang akan

terikat nama output stream.

File 5,9 Masukkan: Mengubah Input Current Stream

Input stream yang aktif dapat diubah dengan menggunakan see/ 1 predikat. Ini membutuhkan

argumen tunggal, yang merupakan atom atau variabel yang mewakili nama file, misalnya

see( ‘myfile.txt’).

Mengevaluasi sebuah tujuan see menyebabkan file bernama input yang menjadi stream.

Jika file ini belum terbuka itu pertama kali dibuka (untuk akses baca saja). Jika tidak

mungkin untuk membuka file dengan nama yang diberikan, kesalahan akan dihasilkan.

Catatan bahwa file yang sesuai dengan arus input yang sebelumnya tetap

terbuka ketika sebuah arus input yang baru dipilih. Hanya arus input

dapat ditutup. Default input stream user, yaitu user. Nilai ini dapat

dipulihkan baik dengan menggunakan dilihat predikat atau dengan see (user).

Built-in predikat see/ 1 memerlukan satu argumen, yang harus menjadi variabel

dan biasanya akan terikat. Mengevaluasi sebuah tujuan see menyebabkan variabel yang akan

terikat nama input stream.

5.9.1 Membaca dari File: End of File

Jika akhir file ditemukan ketika mengevaluasi tujuan read (X), variabel X akan

terikat ke atom end_of_file.

Jika akhir file ditemukan saat mengevaluasi tujuan get(X) atau get0 (X),

variabel X akan terikat kepada seorang ‘khusus’ nilai numerik. Sebagai nilai-nilai ASCII harus dalam

kisaran 0-255 inklusif, ini biasanya akan menjadi -1, tetapi dapat bervariasi dari satu

Prolog pelaksanaan lain.

5.9.2 Membaca dari File: End of Record

Tergantung pada versi Prolog digunakan, mungkin ada ketidakcocokan untuk

karakter input antara membaca akhir sebuah catatan dari terminal user dan dari sebuah file.

Biasanya akhir baris dari input pada terminal user akan ditunjukkan oleh

karakter dengan nilai ASCII 13. Akhir sebuah catatan dalam sebuah file umumnya akan

ditunjukkan oleh dua nilai ASCII: 13 diikuti oleh 10.

Program berikut menunjukkan bagaimana membaca dalam serangkaian karakter dari

keyboard dan mencetak mereka keluar, satu per baris.

Readline:-get0 (X), proses (X).

proses (13).

proses (X):-X = \ = 13, memakai (X), nl, Readline.

Perhatikan useran meletakkan daripada menulis dan bahwa tes untuk nilai ASCII 13

menghindari kebutuhan untuk karakter seperti * untuk menunjukkan ‘akhir input’.

? – Readline.

: Prolog test

Pr

ol

og

t

est

ya

5,10 Contoh Menggunakan File

Menetapkan predikat readterms membaca empat istilah yang pertama dari file tertentu dan

output mereka untuk file ditentukan lain, satu per baris.

Yang sesuai definisi yang diberikan di bawah ini.

readterms (infile, OUTFILE): –

see (infile), seen (OUTFILE),

read (T1), write (T1), nl, read (T2), write (T2), nl,

read (T3),write (T3), nl, read (T4), write (T4), nl,

seen, told.

Dengan asumsi isi file textfile.txt adalah tiga baris:

‘Istilah pertama’. ‘kedua kalinya’.

‘masa jabatan ketiga’.

‘keempat istilah’. ‘kelima istilah’.

menggunakan readterms memberikan output singkat berikut:

? – Readterms ( ‘textfile.txt’, ‘outfile.txt’).

ya

dan membuat sebuah file dengan empat baris teks

Istilah pertama

jabatan kedua

jabatan ketiga

istilah keempat

Meskipun definisi readterms di atas adalah benar sejauh it goes, akhir

Dua istilah (see dan tell) akan menyebabkan stream input dan output harus ditetapkan

user. Ini dapat menyebabkan masalah jika readterms digunakan sebagai subgoal yang lebih besar

program dimana input dan output streamtidak selalu baik user

ketika dipanggil.

Ini adalah praktek pemrograman yang baik untuk mengembalikan asli input dan output stream

sebagai langkah-langkah akhir ketika tujuan seperti readterms dievaluasi. Hal ini dapat dicapai

untuk input dengan menempatkan tujuan see (S) dan see (S) sebelum dan setelah istilah lainnya

dalam tubuh dari sebuah aturan. Mantan mengikat S untuk nama stream input;

yang terakhir me-reset input stream untuk S.

Dampak yang sama dapat dicapai untuk memperoleh output dengan menempatkan tujuan memberitahu (T) dan

kirim (T) sebelum dan sesudah istilah lain dalam tubuh sebuah aturan. Mantan mengikat T

nama output stream; yang terakhir arus output reset stream untuk

T.

Dengan menggunakan konvensi ini, yang telah direvisi readterms definisi adalah sebagai berikut:

readterms (infile, Output): –

see (S), lihat (infile), mengatakan (T), mengatakan (OUTFILE),

read (T1), write(T1), nl, read (T2), write (T2), nl,

read (T3), write (T3), nl, read (T4), write (T4), nl,

see, see (S), told, tell (T).

Expert systems for mortgages

An expert system for mortgages is a computer program that contains the knowledge and analytical skills of human experts, related to mortgage banking. Loan departments are interested in expert systems for mortgages because of the growing cost of labor which makes the handling and acceptance of relatively small loans less profitable. They also see in the application of expert systems a possibility for standardized, efficient handling of mortgage loans, and appreciate that for the acceptance of mortgages there are hard and fast rules which do not always exist with other types of loans.
Since most interest rates for mortgages are controlled by the goverment, intense competition sees to it that a great deal in terms of business depends on the quality of service offered to clients - who shop around for the loan best suiting their needs. Expert systems for mortgages considers the key factors which enter the profitability equation. For instance, “part and parcel of the quality of a mortgage loans portfolio to the bank is the time which elapses between the first contact with the customer and the bank's offering of a loan. Another key ingredient is the fact that home loans have significant features which are not always exploited through classical DP approaches. The expert system corrects this failure” [1].
The expert system also capitalizes on regulatory possibilities. In France, the government subsidizes one type of loan which is available only on low-cost properties (the HLM) and to lower income families. Known as "frets Conventionnes", these carry a rate of interest lower than the rate on the ordinary property loan from a bank. The difficulty is that granting them is subject to numerous regulations, concerning both:
the home which is to be purchased, and
the financial circumstances of the borrower.
To assure that all conditions have been met, every application has to be first processed at branch level and then sent to a central office for checking, before going back to the branch, often with requests for more information from the applicant. This leads to frustrating delays. Expert system for mortgages takes care of these by providing branch employees with tools permitting them to process an application correctly, even if a bank employee does not have an exact knowledge of the screening procedure.

OPERATOR AND ARITHMETIC

Practical Exercise 4
(1) This program is based on Animals Program 3, given in Chapter 2.
dog(fido). large(fido).
cat(mary). large(mary).
dog(rover). small(rover).
cat(jane). small(jane).
dog(tom). small(tom).
cat(harry).
dog(fred). large(fred).
cat(henry). large(henry).
cat(bill).
cat(steve). large(steve).
large(jim).
large(mike).
large_dog(X):- dog(X),large(X).
small_animal(A):- dog(A),small(A).
small_animal(B):- cat(B),small(B).
chases(X,Y):-
large_dog(X),small_animal(Y),
write(X),write(' chases '),write(Y),nl.

Convert the seven predicates used to operator form and test your revised program.
The output should be the same as the output from the program above. Include
directives to define the operators in your program.


(2) Define and test a predicate which takes two arguments, both numbers, and
calculates and outputs the following values: (a) their average, (b) the square root of
their product and (c) the larger of (a) and (b).

OPERATORS AND ARITHMATICS IN PROLOG

Operators and Arithmetics
Operators
Up to now, Prolog user usually use the notation for predicates by a number of arguments in parentheses.
Ex : likes(john,mary)
There is another alternative :
- Two arguments (a binary predicates) be converted to an infix operator
the functor be written between two arguments with no parentheses
Ex : john likes mary
- One argument (a unary predicate) be converted to :
1. Prefix operator  the functor be written before the argument with no parentheses
Ex : isa_dog fred
2. Postfix operator  the functor be written after the argument
Ex : fred isa_dog
Both of predicate (one or two arguments) can be converted to an operator by entering a goal using the op predicate at the system prompt. Ex :
?-op(150,xfy,likes).
This predicate takes three arguments :
1. 150 (operator precedence) : an integer from 0 upwards
So we can change it with another integer.
2. Xfy : the predicate is binary and is to be converted to an infix operator.
This argument should normally be one of the following three atoms:
1. Xfy
2. Fy : the predicate is unary and is to be converted to an prefix operator
3. Xf : the predicate is unary and is to be converted to a postfix operator
3. Likes : the name of the predicate that is to be converted to an operator.
Arithmetics
Prolog user can doing arithmetic calculate with prolog, such as :
1. Arithmetic operator
X+Y : sum of X and Y
X-Y : dfference of X and Y
X*Y : product of X and Y
X/Y : quotient of X and Y
X//Y : the ‘integer quotient’ of X and Y (the result is truncated to the nearest integerbetween it and zero)
X^Y : X to the power of Y
-X : negative of X
abs(X) : absolute value of X
sin(X) : sine of X
cos(X) : cosine of X
max(X,Y) : yhe larger of X and Y
sqrt(X) : square root of X
2. Operator presedence in arithmetic expression
Prolog use ordinary algebra algorithm in arithmetic operation.
Ex : A+B*C-D
In the algebra, C*B are calculate first, then the result+A, then the result of sum-D. it is same with those in prolog. But, if we want to calculate A+B, C-D, then multiply both of the result, we must add the parentheses.
Ex : (A+B)*(C-D)
3. Relasion operator
The operator like =,!=, >, >=, <, <= can be used in prolog

Degree operator
Under is the rist of equality operators that used in prolog with the function of each operator:
• Arithmetic Expression Equality ( =:= )
• Arithmetic Expression Inequality ( =\= )
• Terms Identical ( == )
• Terms Not Identical ( \== )
• Terms Identical With Unification ( = )
• Non-Unification Between Two Terms( \= )

Logic operator
a. Operator NOT
Operator not can be placed before predicate to give the negation. Predicate that be negation has the truth value if the origin predicate is false and has the false value if the origin predicate is truth.
The example of using operator not :
dog(fido).
?- not dog(fido).
no
?- dog(fred).
no
?- not dog(fred).
Yes
b. Disjunction operator
Disjunction operator is used as operator ‘atau’.
Ex :
?- 6<3;7 is 5+2.
yes
?- 6*6=:=36;10=8+3.
yes

Fact, Rules, Predicate, and Variable in Prolog

1. Buka Notepad

2. Tulis database animal di notepad.

3. Simpan dengan format (nama.pl)

4. Buka prolog

5. Pilih file-consult-pilih file yang dituju

6. Tuliskan query(pertanyaan)

Contoh :

a) Sebutkan semua mamalia ! (gambar soal a)

b) Sebutkan semua binatang karnivora yang termasuk mamalia ! (gambar soal b)

c) Sebutkan semua mamalia yang belang (gambar soal c)

d) Apakah ada reptil yang memiliki bulu tengkuk? (gambar soal d)

Dan lihat hasilnya

Literatur Review

Expert systems topics

[edit] Chaining

There are two main methods of reasoning when using inference rules: backward chaining and forward chaining.

Forward chaining starts with the data available and uses the inference rules to conclude more data until a desired goal is reached. An inference engine using forward chaining searches the inference rules until it finds one in which the if clause is known to be true. It then concludes the then clause and adds this information to its data. It would continue to do this until a goal is reached. Because the data available determines which inference rules are used, this method is also called data driven.

Backward chaining starts with a list of goals and works backwards to see if there is data which will allow it to conclude any of these goals. An inference engine using backward chaining would search the inference rules until it finds one which has a then clause that matches a desired goal. If the ifrule base contains clause of that inference rule is not known to be true, then it is added to the list of goals. For example, suppose a

1. If Fritz is green then Fritz is a frog.
2. If Fritz is a frog then Fritz hops.

Suppose a goal is to conclude that Fritz hops. The rule base would be searched and rule (2) would be selected because its conclusion (the then clause) matches the goal. It is not known that Fritz is a frog, so this "if" statement is added to the goal list. The rule base is again searched and this time rule (1) is selected because its then clause matches the new goal just added to the list. This time, the if clause (Fritz is green) is known to be true and the goal that Fritz hops is concluded. Because the list of goals determines which rules are selected and used, this method is called goal driven.

[edit] Certainty factors

One advantage of expert systems over traditional methods of programming is that they allow the use of "confidences", also known as certainty factors. A human, when reasoning, does not always conclude things with 100% confidence: he might venture, "If Fritz is green, then he is probably a frog" (after all, he might be a chameleon). This type of reasoning can be imitated by using numeric values called confidences. For example, if it is known that Fritz is green, it might be concluded with 0.85 confidence that he is a frog; or, if it is known that he is a frog, it might be concluded with 0.95 confidence that he hops. These numbers are probabilities in a Bayesian sense, in that they quantify uncertainty.

[edit] Expert system architecture

The following general points about expert systems and their architecture have been illustrated.

1. The sequence of steps taken to reach a conclusion is dynamically synthesized with each new case. It is not explicitly programmed when the system is built.

2. Expert systems can process multiple values for any problem parameter. This permits more than one line of reasoning to be pursued and the results of incomplete (not fully determined) reasoning to be presented.

3. Problem solving is accomplished by applying specific knowledge rather than specific technique. This is a key idea in expert systems technology. It reflects the belief that human experts do not process their knowledge differently from others, but they do possess different knowledge. With this philosophy, when one finds that their expert system does not produce the desired results, work begins to expand the knowledge base, not to re-program the procedures.

There are various expert systems in which a rulebase and an inference engine cooperate to simulate the reasoning process that a human expert pursues in analyzing a problem and arriving at a conclusion. In these systems, in order to simulate the human reasoning process, a vast amount of knowledge needed to be stored in the knowledge base. Generally, the knowledge base of such an expert system consisted of a relatively large number of "if then" type of statements that were interrelated in a manner that, in theory at least, resembled the sequence of mental steps that were involved in the human reasoning process.

Because of the need for large storage capacities and related programs to store the rulebase, most expert systems have, in the past, been run only on large information handling systems. Recently, the storage capacity of personal computers has increased to a point where it is becoming possible to consider running some types of simple expert systems on personal computers.

In some applications of expert systems, the nature of the application and the amount of stored information necessary to simulate the human reasoning process for that application is just too vast to store in the active memory of a computer. In other applications of expert systems, the nature of the application is such that not all of the information is always needed in the reasoning process. An example of this latter type application would be the use of an expert system to diagnose a data processing system comprising many separate components, some of which are optional. When that type of expert system employs a single integrated rulebase to diagnose the minimum system configuration of the data processing system, much of the rulebase is not required since many of the components which are optional units of the system will not be present in the system. Nevertheless, earlier expert systems require the entire rulebase to be stored since all the rules were, in effect, chained or linked together by the structure of the rulebase.

When the rulebase is segmented, preferably into contextual segments or units, it is then possible to eliminate portions of the Rulebase containing data or knowledge that is not needed in a particular application. The segmenting of the rulebase also allows the expert system to be run with systems or on systems having much smaller memory capacities than was possible with earlier arrangements since each segment of the rulebase can be paged into and out of the system as needed. The segmenting of the rulebase into contextual segments requires that the expert system manage various intersegment relationships as segments are paged into and out of memory during execution of the program. Since the system permits a rulebase segment to be called and executed at any time during the processing of the first rulebase, provision must be made to store the data that has been accumulated up to that point so that at some time later in the process, when the system returns to the first segment, it can proceed from the last point or rule node that was processed. Also, provision must be made so that data that has been collected by the system up to that point can be passed to the second segment of the rulebase after it has been paged into the system and data collected during the processing of the second segment can be passed to the first segment when the system returns to complete processing that segment.

The user interface and the procedure interface are two important functions in the information collection process.

[edit] End user

The end-user usually sees an expert system through an interactive dialog, an example of which follows:

Q. Do you know which restaurant you want to go to?

A. No

Q. Is there any kind of food you would particularly like?

A. No

Q. Do you like spicy food?

A. No

Q. Do you usually drink wine with meals?

A. Yes

Q. When you drink wine, is it French wine?

A. Yes

As can be seen from this dialog, the system is leading the user through a set of questions, the purpose of which is to determine a suitable set of restaurants to recommend. This dialog begins with the system asking if the user already knows the restaurant choice (a common feature of expert systems) and immediately illustrates a characteristic of expert systems; users may choose not to respond to any question. In expert systems, dialogs are not pre-planned. There is no fixed control structure. Dialogs are synthesized from the current information and the contents of the knowledge base. Because of this, not being able to supply the answer to a particular question does not stop the consultation.

[edit] Explanation system

Another major distinction between expert systems and traditional systems is illustrated by the following answer given by the system when the user answers a question with another question, "Why", as occurred in the above example. The answer is:

A. I am trying to determine the type of restaurant to suggest. So far Chinese is not a likely choice. It is possible that French is a likely choice. I know that if the diner is a wine drinker, and the preferred wine is French, then there is strong evidence that the restaurant choice should include French.

It is very difficult to implement a general explanation system (answering questions like "Why" and "How") in a traditional computer program. An expert system can generate an explanation by retracing the steps of its reasoning. The response of the expert system to the question WHY is an exposure of the underlying knowledge structure. It is a rule; a set of antecedent conditions which, if true, allow the assertion of a consequent. The rule references values, and tests them against various constraints or asserts constraints onto them. This, in fact, is a significant part of the knowledge structure. There are values, which may be associated with some organizing entity. For example, the individual diner is an entity with various attributes (values) including whether they drink wine and the kind of wine. There are also rules, which associate the currently known values of some attributes with assertions that can be made about other attributes. It is the orderly processing of these rules that dictates the dialog itself.

[edit] Expert systems versus problem-solving systems

The principal distinction between expert systems and traditional problem solving programs is the way in which the problem related expertise is coded. In traditional applications, problem expertise is encoded in both program and data structures. In the expert system approach all of the problem related expertise is encoded in data structures only; no problem-specific information is encoded in the program structure. This organization has several benefits.

An example may help contrast the traditional problem solving program with the expert system approach. The example is the problem of tax advice. In the traditional approach data structures describe the taxpayer and tax tables, and a program in which there are statements representing an expert tax consultant's knowledge, such as statements which relate information about the taxpayer to tax table choices. It is this representation of the tax expert's knowledge that is difficult for the tax expert to understand or modify.

In the expert system approach, the information about taxpayers and tax computations is again found in data structures, but now the knowledge describing the relationships between them is encoded in data structures as well. The programs of an expert system are independent of the problem domain (taxes) and serve to process the data structures without regard to the nature of the problem area they describe. For example, there are programs to acquire the described data values through user interaction, programs to represent and process special organizations of description, and programs to process the declarations that represent semantic relationships within the problem domain and an algorithm to control the processing sequence and focus.

The general architecture of an expert system involves two principal components: a problem dependent set of data declarations called the knowledge base or rule base, and a problem independent (although highly data structure dependent) program which is called the inference engine.

[edit] Individuals involved with expert systems

There are generally three individuals having an interaction with expert systems. Primary among these is the end-user; the individual who uses the system for its problem solving assistance. In the building and maintenance of the system there are two other roles: the problem domain expert who builds and supplies the knowledge base providing the domain expertise, and a knowledge engineer who assists the experts in determining the representation of their knowledge, enters this knowledge into an explanation module and who defines the inference technique required to obtain useful problem solving activity. Usually, the knowledge engineer will represent the problem solving activity in the form of rules which is referred to as a rule-based expert system. When these rules are created from the domain expertise, the knowledge base stores the rules of the expert system.

[edit] Inference rule

An understanding of the "inference rule" concept is important to understand expert systems. An inference rule is a statement that has two parts, an if clause and a then clause. This rule is what gives expert systems the ability to find solutions to diagnostic and prescriptive problems. An example of an inference rule is:

If the restaurant choice includes French, and the occasion is romantic,

Then the restaurant choice is definitely Paul Bocuse.

An expert system's rulebase is made up of many such inference rules. They are entered as separate rules and it is the inference engine that uses them together to draw conclusions. Because each rule is a unit, rules may be deleted or added without affecting other rules (though it should affect which conclusions are reached). One advantage of inference rules over traditional programming is that inference rules use reasoning which more closely resemble human reasoning.

Thus, when a conclusion is drawn, it is possible to understand how this conclusion was reached. Furthermore, because the expert system uses knowledge in a form similar to the expert, it may be easier to retrieve this information from the expert.

[edit] Procedure node interface

The function of the procedure node interface is to receive information from the procedures coordinator and create the appropriate procedure call. The ability to call a procedure and receive information from that procedure can be viewed as simply a generalization of input from the external world. While in some earlier expert systems external information has been obtained, that information was obtained only in a predetermined manner so only certain information could actually be acquired. This expert system, disclosed in the cross-referenced application, through the knowledge base, is permitted to invoke any procedure allowed on its host system. This makes the expert system useful in a much wider class of knowledge domains than if it had no external access or only limited external access.

In the area of machine diagnostics using expert systems, particularly self-diagnostic applications, it is not possible to conclude the current state of "health" of a machine without some information. The best source of information is the machine itself, for it contains much detailed information that could not reasonably be provided by the operator.

The knowledge that is represented in the system appears in the rulebase. In the rulebase described in the cross-referenced applications, there are basically four different types of objects, with associated information present.

1. Classes—these are questions asked to the user.
2. Parameters—a parameter is a place holder for a character string which may be a variable that can be inserted into a class question at the point in the question where the parameter is positioned.
3. Procedures—these are definitions of calls to external procedures.
4. Rule Nodes—The inferencing in the system is done by a tree structure which indicates the rules or logic which mimics human reasoning. The nodes of these trees are called rule nodes. There are several different types of rule nodes.

The rulebase comprises a forest of many trees. The top node of the tree is called the goal node, in that it contains the conclusion. Each tree in the forest has a different goal node. The leaves of the tree are also referred to as rule nodes, or one of the types of rule nodes. A leaf may be an evidence node, an external node, or a reference node.

An evidence node functions to obtain information from the operator by asking a specific question. In responding to a question presented by an evidence node, the operator is generally instructed to answer "yes" or "no" represented by numeric values 1 and 0 or provide a value of between 0 and 1, represented by a "maybe."

Questions which require a response from the operator other than yes or no or a value between 0 and 1 are handled in a different manner.

A leaf that is an external node indicates that data will be used which was obtained from a procedure call.

A reference node functions to refer to another tree or subtree.

A tree may also contain intermediate or minor nodes between the goal node and the leaf node. An intermediate node can represent logical operations like And or Or.

The inference logic has two functions. It selects a tree to trace and then it traces that tree. Once a tree has been selected, that tree is traced, depth-first, left to right.

The word "tracing" refers to the action the system takes as it traverses the tree, asking classes (questions), calling procedures, and calculating confidences as it proceeds.

As explained in the cross-referenced applications, the selection of a tree depends on the ordering of the trees. The original ordering of the trees is the order in which they appear in the rulebase. This order can be changed, however, by assigning an evidence node an attribute "initial" which is described in detail in these applications. The first action taken is to obtain values for all evidence nodes which have been assigned an "initial" attribute. Using only the answers to these initial evidences, the rules are ordered so that the most likely to succeed is evaluated first. The trees can be further re-ordered since they are constantly being updated as a selected tree is being traced.

It has been found that the type of information that is solicited by the system from the user by means of questions or classes should be tailored to the level of knowledge of the user. In many applications, the group of prospective uses is nicely defined and the knowledge level can be estimated so that the questions can be presented at a level which corresponds generally to the average user. However, in other applications, knowledge of the specific domain of the expert system might vary considerably among the group of prospective users.

One application where this is particularly true involves the use of an expert system, operating in a self-diagnostic mode on a personal computer to assist the operator of the personal computer to diagnose the cause of a fault or error in either the hardware or software. In general, asking the operator for information is the most straightforward way for the expert system to gather information assuming, of course, that the information is or should be within the operator's understanding. For example, in diagnosing a personal computer, the expert system must know the major functional components of the system. It could ask the operator, for instance, if the display is a monochrome or color display. The operator should, in all probability, be able to provide the correct answer 100% of the time. The expert system could, on the other hand, cause a test unit to be run to determine the type of display. The accuracy of the data collected by either approach in this instance probably would not be that different so the knowledge engineer could employ either approach without affecting the accuracy of the diagnosis. However, in many instances, because of the nature of the information being solicited, it is better to obtain the information from the system rather than asking the operator, because the accuracy of the data supplied by the operator is so low that the system could not effectively process it to a meaningful conclusion.

In many situations the information is already in the system, in a form of which permits the correct answer to a question to be obtained through a process of inductive or deductive reasoning. The data previously collected by the system could be answers provided by the user to less complex questions that were asked for a different reason or results returned from test units that were previously run.

[edit] User interface

The function of the user interface is to present questions and information to the user and supply the user's responses to the inference engine.

Any values entered by the user must be received and interpreted by the user interface. Some responses are restricted to a set of possible legal answers, others are not. The user interface checks all responses to insure that they are of the correct data type. Any responses that are restricted to a legal set of answers are compared against these legal answers. Whenever the user enters an illegal answer, the user interface informs the user that his answer was invalid and prompts him to correct it.

[edit] Application of expert systems

Expert systems are designed and created to facilitate tasks in the fields of accounting, medicine, process control, financial service, production, human resources etc. Indeed, the foundation of a successful expert system depends on a series of technical procedures and development that may be designed by certain technicians and related experts.

A good example of application of expert systems in banking area is expert systems for mortgages. Loan departments are interested in expert systems for mortgages because of the growing cost of labour which makes the handling and acceptance of relatively small loans less profitable. They also see in the application of expert systems a possibility for standardised, efficient handling of mortgage loan, and appreciate that for the acceptance of mortgages there are hard and fast rules which do not always exist with other types of loans.

While expert systems have distinguished themselves in AI research in finding practical application, their application has been limited. Expert systems are notoriously narrow in their domain of knowledge—as an amusing example, a researcher used the "skin disease" expert system to diagnose his rustbucket car as likely to have developed measles—and the systems were thus prone to making errors that humans would easily spot. Additionally, once some of the mystique had worn off, most programmers realized that simple expert systems were essentially just slightly more elaborate versions of the decision logic they had already been using. Therefore, some of the techniques of expert systems can now be found in most complex programs without any fuss about them.

An example, and a good demonstration of the limitations of, an expert system used by many people is the Microsoft Windows operating system troubleshooting software located in the "help" section in the taskbar menu. Obtaining expert/technical operating system support is often difficult for individuals not closely involved with the development of the operating system. Microsoft has designed their expert system to provide solutions, advice, and suggestions to common errors encountered throughout using the operating systems.

Another 1970s and 1980s application of expert systems — which we today would simply call AI — was in computer games. For example, the computer baseball games Earl Weaver Baseball and Tony La Russa Baseball each had highly detailed simulations of the game strategies of those two baseball managers. When a human played the game against the computer, the computer queried the Earl Weaver or Tony La Russa Expert System for a decision on what strategy to follow. Even those choices where some randomness was part of the natural system (such as when to throw a surprise pitch-out to try to trick a runner trying to steal a base) were decided based on probabilities supplied by Weaver or La Russa. Today we would simply say that "the game's AI provided the opposing manager's strategy."

[edit] Advantages and disadvantages

Advantages:

• Provides consistent answers for repetitive decisions, processes and tasks
• Holds and maintains significant levels of information
• Encourages organizations to clarify the logic of their decision-making
• Never "forgets" to ask a question, as a human might
• Can work round the clock
• Can be used by the user more frequently
• A multi-user expert system can serve more users at a time

Disadvantages:

• Lacks common sense needed in some decision making
• Cannot make creative responses as human expert would in unusual circumstances
• Domain experts not always able to explain their logic and reasoning
• Errors may occur in the knowledge base, and lead to wrong decisions
• Cannot adapt to changing environments, unless knowledge base is changed

[edit] Types of problems solved by expert systems

Expert systems are most valuable to organizations that have a high-level of know-how experience and expertise that cannot be easily transferred to other members. They are designed to carry the intelligence and information found in the intellect of experts and provide this knowledge to other members of the organization for problem-solving purposes.

Typically, the problems to be solved are of the sort that would normally be tackled by a medical or other professional. Real experts in the problem domain (which will typically be very narrow, for instance "diagnosing skin conditions in human teenagers") are asked to provide "rules of thumb" on how they evaluate the problems, either explicitly with the aid of experienced systems developers, or sometimes implicitly, by getting such experts to evaluate test cases and using computer programs to examine the test data and (in a strictly limited manner) derive rules from that. Generally, expert systems are used for problems for which there is no single "correct" solution which can be encoded in a conventional algorithm — one would not write an expert system to find shortest paths through graphs, or sort data, as there are simply easier ways to do these tasks.

Simple systems use simple true/false logic to evaluate data. More sophisticated systems are capable of performing at least some evaluation, taking into account real-world uncertainties, using such methods as fuzzy logic. Such sophistication is difficult to develop and still highly imperfect.Expert Systems Shells or Inference Engine

A shell is a complete development environment for building and maintaining knowledge-based applications. It provides a step-by-step methodology, and ideally a user-friendly interface such as a graphical interface, for a knowledge engineer that allows the domain experts themselves to be directly involved in structuring and encoding the knowledge. Many commercial shells are available, one example being eGanges which aims to remove the need for a knowledge engineer.

http://en.wikipedia.org/wiki/Expert_system