kontrak perkuliahan deskripsi tujuan referensi kriteria penilaian

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12/1/2009 1 KECERDASAN BUATAN KULIAH 01 - PENDAHULUAN Yeni Herdiyeni Departemen Ilmu Komputer FMIPA IPB http://www.ilkom.fmipa.ipb.ac.id/~yeni Kontrak Perkuliahan Nama Mata Kuliah : Kecerdasan Buatan Kode Mata Kuliah : KOM321 Beban Kredit : 3(3-0) Semester : Gasal, 2009/2010 Pengajar : Yeni Herdiyeni, S.Si. M.Komp (YHY) Mushtofa, S.Komp., MSc. (MUS) Deskripsi Pembahasan dalam matakuliah ini dimulai dengan posisi dan ruang lingkup artificial intelligent. Dilanjutkan dengan domain permasalahan, berbagai metode searching, berbagai representasi pengetahuan, matching, metode inferensi (secara statistik, bayes, maupun fuzzy), dan diakhiri dengan pembahasan mengenai soft computing dengan tiga topik utama yaitu : neural network, fuzzy system, dan algoritma genetika. Tujuan Mahasiswa mampu menjelaskan sistem kecerdasasan buatan serta mampu merepresentasikan pengetahuan dan menjelaskan metode inferensia pengambilan kesimpulan Referensi Russell S. & Peter N. 2003. Artificial Intelligence: A Modern Approach. Edisi ke- 2. Prentice-Hall, New Jersey. Kriteria Penilaian Nilai akhir (NA) adalah nilai kumulatif dari nilai ujian tengah semester (UTS), ujian akhir semester (UAS), tugas perorangan (TP), dan tugas kelompok atau proyek akhir (PA). Metode dan bobot nilai sebagai berikut: UTS (1‐6) dan UAS (7‐14) dilakukan melalui ujian tertulis dengan bobot masing‐masing 35%. Kisi‐kisi ujian akan disampaikan pada pertemuan ke‐6 untuk UTS, dan pada pertemuan ke‐14 untuk UAS. Nilai TP adalah rata‐rata dari semua tugas yang diberikan, dan diberi bobot 10% Nilai PA terdiri dari nilai produk proyek (program komputer, laporan) dan presentasi. Bobot nilai PA adalah 20%.

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Page 1: Kontrak Perkuliahan Deskripsi Tujuan Referensi Kriteria Penilaian

12/1/2009

1

KECERDASAN BUATAN

KULIAH 01 - PENDAHULUAN

Yeni Herdiyeni

Departemen Ilmu Komputer FMIPA IPB

http://www.ilkom.fmipa.ipb.ac.id/~yeni

Kontrak Perkuliahan

• Nama Mata Kuliah : Kecerdasan Buatan

• Kode Mata Kuliah : KOM321

• Beban Kredit : 3(3-0)

• Semester : Gasal, 2009/2010

• Pengajar :

– Yeni Herdiyeni, S.Si. M.Komp (YHY)

– Mushtofa, S.Komp., MSc. (MUS)

Deskripsi

• Pembahasan dalam matakuliah ini dimulai dengan posisi dan ruang lingkup artificial intelligent. Dilanjutkan dengan domain permasalahan, berbagai metode searching, berbagai representasi pengetahuan, matching, metode inferensi (secara statistik, bayes, maupun fuzzy), dan diakhiri dengan pembahasan mengenai soft computing dengan tiga topik utama yaitu : neural network, fuzzy system, dan algoritma genetika.

Tujuan

• Mahasiswa mampu menjelaskan sistem kecerdasasan buatan serta mampu merepresentasikan pengetahuan dan menjelaskan metode inferensia pengambilan kesimpulan

Referensi

• Russell S. & Peter N. 2003. Artificial Intelligence: A Modern Approach. Edisi ke-2. Prentice-Hall, New Jersey.

Kriteria Penilaian

• Nilai akhir (NA) adalah nilai kumulatif dari nilai ujian tengah semester (UTS), ujian akhir semester (UAS), tugas perorangan (TP), dan tugas kelompok atau proyek akhir (PA). Metode dan bobot nilai sebagai berikut:

• UTS (1‐6) dan UAS (7‐14) dilakukan melalui ujian tertulis dengan bobot masing‐masing 35%. Kisi‐kisi ujian akan disampaikan pada pertemuan ke‐6 untuk UTS, dan pada pertemuan ke‐14 untuk UAS.

• Nilai TP adalah rata‐rata dari semua tugas yang diberikan, dan diberi bobot 10%

• Nilai PA terdiri dari nilai produk proyek (program komputer, laporan) dan presentasi. Bobot nilai PA adalah 20%.

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12/1/2009

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Topik

1. Kuliah 01 - Pendahuluan 2. Kuliah 02 - Penelusuran 3. Kuliah 03 & 04 : Teknik Penelusuran 4. Kuliah 05 & 06 :Agen berbasis logika preposisi 5. Kuliah 07 - Studi Kasus 6. Kuliah 08 & 09 : Agen berbasis logika predikat orde satu (FOL) 7. Kuliah 10 : Reasoning : Statistical Reasoning I (Probabilitas Bayes)8. Kuliah 11 & 12 : Reasoning : Statistical Reasoning II (Bayesian

Networks)9. Kuliah 13 :Machine Learning :10. Kuliah 14 : Studi Kasus

Apakah Kecerdasan Buatan itu?

How doesthe humanbrain work?

How do weemulate the

human brain?

Who cares? Let’sdo some cool and

useful stuff!

How do wecreate

intelligence?What is

intelligence?

How do we classify research as AI?

Does itinvestigatethe brain?

If we don’t know howit works, then it’s AI.

When we find outhow it works, it’s not

AI anymore…

Is it

intelligent?Does itinvestigate

intelligence?

Does it emulatethe brain?

Why study AI?

Search engines

Labor

Science

Medicine/Diagnosis

Appliances What else?

Honda Humanoid Robot

Walk

Turn

Stairshttp://world.honda.com/robot/

Sony AIBO

http://www.aibo.com

Page 3: Kontrak Perkuliahan Deskripsi Tujuan Referensi Kriteria Penilaian

12/1/2009

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Natural Language Question Answering

http://www.ai.mit.edu/projects/infolab/http://aimovie.warnerbros.com

What is AI?• Various definitions:

– Building intelligent entities.

– Getting computers to do tasks which require human intelligence.

• But what is “intelligence”?

• Simple things turn out to be the hardest to automate:– Recognising a face.

– Navigating a busy street.

– Understanding what someone says.

• All tasks require reasoning on knowledge.

Why do AI?

• Two main goals of AI:

– To understand human intelligence better. We test theories of human intelligence by writing programs which emulate it.

– To create useful “smart” programs able to do tasks that would normally require a human expert.

Who does AI?

• Many disciplines contribute to goal of creating/modelling intelligent entities:

– Computer Science

– Psychology (human reasoning)

– Philosophy (nature of belief, rationality, etc)

– Linguistics (structure and meaning of language)

– Human Biology (how brain works)

• Subject draws on ideas from each discipline.

Definisi Kecerdasan Buatan

The exciting new effort to make computers thinks … machine with minds, in the full and literal sense” (Haugeland 1985)

“The art of creating machines that perform functions that require intelligence when performed by people” (Kurzweil, 1990)

“The study of mental faculties through the use of computational models” (Charniak et al. 1985)

A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes” (Schalkol, 1990)

Systems that think like humans Systems that think rationally

Systems that act like humans Systems that act rationally

Approaches to AI

• Searching

• Learning

• From Natural to Artificial Systems

• Knowledge Representation and Reasoning

• Expert Systems and Planning

• Communication, Perception, Action

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Search

• “All AI is search”

– Game theory

– Problem spaces

• Every problem is a feature space of all possible (successful or unsuccessful) solutions.

• The trick is to find an efficient search strategy.

Learning

• Explanation

– Discovery

– Data Mining

• No Explanation

– Neural Nets

– Case Based Reasoning

Learning: Explanation

• Cases to rules

AI with Neural networks

• Introduction to perceptrons, Hopfield networks, self-organizing feature maps. How to size a network? What can neural networks achieve?

x (t)1

x (t)n

x (t)2

y(t+1)

w1

2

n

w

w

axon

Approaches to AI

• Searching

• Learning

• From Natural to Artificial Systems

• Knowledge Representation and Reasoning

• Expert Systems and Planning

• Communication, Perception, Action

Genetic Algorithms.Evolving Intelligent Systems

Introduction

to genetic algorithms

and their use in

optimization

problems.

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Approaches to AI

• Searching

• Learning

• From Natural to Artificial Systems

• Knowledge Representation and Reasoning

• Expert Systems and Planning

• Communication, Perception, Action

Rule-Based Systems

• Logic Languages

– Prolog, Lisp

• Knowledge bases

• Inference engines

Rule-Based Languages: Prolog

Father(abraham, isaac). Male(isaac).Father(haran, lot). Male(lot).Father(haran, milcah). Female(milcah).Father(haran, yiscah). Female(yiscah).

Son(X,Y) Father(Y,X), Male(X).Daughter(X,Y) Father(Y,X), Female(X).

Son(lot, haran)?

Ability-Based Areas

• Computer vision

• Natural language recognition

• Natural language generation

• Speech recognition

• Speech generation

• Robotics

Natural Language: Translation

“The flesh is weak, but the spirit is

strong”

Translate to Russian

Translate back to English

“The food was lousy, but the vodka was

great!”

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Natural Language Recognition

You give me the gold

pronounn

verb pronound

article noun

VP NP

VP

NP

VP

NP

sentencew

PERSON:

Joe

PERSON:FredTRANSACTION

GOLD: X

REPT

OBJ

AGNT

Audio

Words

Syntax

Context

Semantics