kontrak perkuliahan deskripsi tujuan referensi kriteria penilaian
TRANSCRIPT
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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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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
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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