Fact, RULES, PREDICATE and VARIABLE (Print Screen) I dan II (NOTEPAD)

NOTEPAD "ANIMAL"

















NOTEPAD "DATING"

Fact, RULES, PREDICATE and VARIABLE (Print Screen) II

Langkah-langkah :

1. setelah menyalin soal dari Exercise 2 ke dalam notepad

2. kemudian save notepad dengan format (.pl)

3. buka dalam program prolog .

4. kerjakan soal tersebut .

5. yaitu mencari pasangan pria dan wanita .

6. Di dalam menjawab jangan lupa tanda titik (.) setelah ENTER jangan lupa tanda titik koma (;). hingga program menunjukkan pernyataan NO.

7. kemudian pada program prolog tersebut akan muncul beberapa pasangan pria dan wanita .

Fact, RULES, PREDICATE and VARIABLE (Print Screen) I

Langkah-langkah :

1. setelah menyalin soal dari Exercise 2 ke dalam notepad

2. kemudian save notepad dengan format (.pl)

3. buka dalam program prolog .

4. kerjakan soal tersebut .

5. a) semua mammalia
b) semua mammalia dan karnivora
c) semua mammalia dengan stripes
d) yang reptilia

6. kemudian jawab ke dalam program prolog. di dalam menjawab jangan lupa sertakan tanda titik (.) setelah memencet ENTER jangan lupa sertakan tanda titik koma (;). hingga program menunjukkan pernyataan NO. kemudian lanjutkan ke dalam soal berikutnya.

Literature Review (Expert System and Rule-based Expert System)

Sistem Pakar berbasis kaidah

Sistem pakar adalah suatu program komputer yang memperlihatkan derajat keahlian dalam pemecahan masalah di bidang tertentu sebanding dengan seorang pakar (Ignizio, 1991). Keahlian sistem pakar dalam memecahkan suatu masalah diperoleh dengan cara merepresentasikan pengetahuan seorang atau beberapa orang pakar dalam format tertentu dan menyimpannya dalam basis pengetahuan. Sistem pakar berbasis kaidah (rule-based expert system) adalah sistem pakar yang menggunakan kaidah (rules) untuk merepresentasikan pengetahuan di dalam basis pengetahuannya.
Mesin inferensi (inference engine) merupakan bagian yang bertindak sebagai pencari solusi dari suatu permasalahan berdasar pada kaidah-kaidah yang ada dalam basis pengetahuan sistem pakar. Selama proses inferensi, mesin inferensi memeriksa status dari basis pengetahuan dan memori kerja (working memory) untuk menentukan fakta apa saja yang diketahui dan untuk menambahkan fakta baru yang dihasilkan ke dalam memori kerja tersebut. Fakta-fakta yang merupakan hasil dari proses inferensi disimpan dalam memori kerja.

Literature Review (Expert System and Rule-based Expert System)

Expert systems topics

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 if clause of that inference rule is not known to be true, then it is added to the list of goals. For example, suppose a rule base contains

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.

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.

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.

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.

Logic Programming

Logic Programming

• Pemrograman deklaratif, mendeklarasikan tujuan

komputasi, bukan menyusun algoritme secara

detil.

• Disebut juga rule‐based programming.

• Aplikasinya:

– Artificial intelligence, misalnya MYCIN

– Database information retrieval, misalnya SQL

• Fitur dari pemrograman logika:

– Nondeterministik, program bisa memperoleh banyak

jawaban, tidak hanya satu jawaban

– Backtracking, lacak balik secara otomatis


Horn clauses

• Program logika menggunakan ekspresi logika

matematika untuk jawaban masalah.

• Dasar pemrograman logika adalah proposisi

dan predikat.

• Salah satu bentuknya adalah Horn clause.

• Definisi:

Horn clause memiliki sebuah head h yang disebut

sebagai predikat, dan body sebagai daftar dari

predikat p1, p2, …., pn.

• Dapat ditulis sebagai

h ← p1, p2, …., pn

Artinya, h adalah benar (true) hanya jika p1, p2, …., pn

secara simultan adalah benar.

• Contoh:

salju(C) ← hujan(C), beku(C)


Logika Predikat

• Disebut juga kalkulus predikat.

• Dinyatakan dalam bentuk fakta dan aturan.

• Contoh:

– lelaki(andi) // fakta

– menikah(andi , ana) // fakta

– ∀y∃x orang(y) → ibu(x,y) // aturan

– ∀x∀y[menikah(x,y) ∧ lelaki(x)] → ~lelaki(y)

• lelaki, menikah, orang, ibu disebut relasi.


Bahasa Deklaratif

• Ari adalah bapak dari Andi, ditulis:

bapak(Ari, Andi).

• A adalah kakek dari F, dibuat aturan:

jika A bapaknya X dan X bapaknya F

atau

jika A bapaknya X dan X ibunya F

ditulis sebagai

kakek(A,F) :‐ bapak(A,X), bapak(X,F).

kakek(A,F) :‐ bapak(A,X), ibu(X,F).


Fakta

• Kenyataan atau kebenaran yang diakui

• Menyatakan:

– Hubungan dua atau lebih obyek

– Sifat obyek

• Contoh:

bapak(ari, andi).

biru(langit).

manis(gula).


Aturan

• Logika yg dirumuskan dalam bentuk relasi

sebab‐akibat dan hubungan implikasi.

• Contoh:

kakek(A,F) :‐ bapak(A,X), bapak(X,F).

kakek(A,F) :‐ bapak(A,X), ibu(X,F).

Apasih PROLOG itu ??

Apa sih PROLOG itu?

Prolog adalah salah satu bahasa pemrograman yang berkembang pesat saat ini. Bahasa ini cocok digunakan untuk menyelesaikan masalah yang berkaitan dengan objek terstruktur dan relasi (hubungan) antara objek-objek tersebut. Contohnya ialah ilustrasi di atas, kita dapat menyatakan hubungan antara tiga buah objek (X, Y dan Z) dengan mudah menggunakan Prolog. Z adalah parent (orang tua) dari X dan Y, serta X adalah sister (saudara perempuan) dari Y. Sehingga dengan kata lain, X dan Y adalah child (anak) dari Z. Lalu, bagaimana kita mengetahui hubungan antara objek-objek tersebut dalam prolog. Dalam prolog ada istilah fact (fakta), rule (aturan), goal (tujuan) dan query(pertanyaan). Dalam tulisan ini akan dijelaskan lebih dalam mengenai konsep dari keempat kata tersebut.

Prolog merupakan singkatan dari Programming in Logic. Idenya berasal dari era
1970-an yang

bertujuan untuk menggunakan logic (logika) sebagai bahasa pemrograman.

PROLOG (TUTORIAL)

OUR BIOGRAPHY

hell-o EVERYBODY . haha

group kami bernama MBOIS
(haha . nama yang
aneh) .
kami memakai nama tersebut karena

kami merasa kita keren (haha . emang kami

sukanya bercanda :D) .


kami berasal dari daerah dan sekolah yang

berbeda-beda . julia berasal dari SMAN 7 , bibah

berasal dari SMAN 16 . mereka berdua

sama-sama berasal dari surabaya . sedangkan

aan berasal dari SMAN 2 jombang dan rendi

berasal dari SMAN 2 lamongan .

kami berharap dengan adanya blog ini . kita semua dapat saling share tentang mata kuliah

MATEMATIKA DISKRIT yang diajari oleh dosen kami yaitu Mr. Ahmad Mukhlason




kami sebenarnya agak ngga 'mudeng' karena semuanya

FULL ENGLISH . mungkin kalo kita pake bahasa indonesia

kita bisa lebih mengerti (hehe . maaf ya , pak . kita emng

ngga 'mudeng' . hehe) .


kami harap blog kami bisa menyambungkan antara grup

kami dengan pak.son juga dengan grup lainnya . seumpama

kami minta pertolongannya agar kami menjadi ANAK

PINTAR .

buat pak.son : komen kita pake bahasa indonesia ya , pak .
dan beri kami nilai bagus :)

♥ PERSONAL BIOGRAPHY ♥

NAMA : Julia Carolina Daud

NRP : 5209100059

Alamat : Jl. Pucang Anom Timur No. 1A

TTL : Surabaya , 13 Juli 1991

Asal : Surabaya

Hobi : Mengerjakan PR (*hehe bohonng)












Nama : Habibah Asma'ul Husna

NRP : 5209100052

Alamat : Jl. Wonorejo I/67

TTL : Surabaya , 19 September 1991

Asal : Surabaya

Hobi : Browsing (haha . LOL)








Nama : Akhmad Anshori Hidayat

NRP : 5209100135

Alamat : Gebang Kidul gg.Puskesmas 14 Sby

TTL : Jombang , 14 Oktober 1990

Asal : Jombang

Hobi : Ngoding













Nama : Rendi Nugroho

NRP : 5209100124

Alamat : Jl. Keputih gg. II/26

TTL : Lamongan , 28 Maret 1991

Asal : Lamongan

Hobi : Sepak Bola

ANGGOTA KELOMPOK "MBOIS"

Habibah Asmaul Husna 5209100052

Julia carolina Daud 5209100059

Rendi Nugroho 5209100124

Akhmad Anshori Hidayat 5209100135