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sistem cerdas | gasal 2014Tim Teaching:Rully Agus Hendrawan,
S.Kom, M.EngIrmasari Hafidz, S.Kom, M.Sc
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Our class SC(A) & SC(B)
JadwalKelas A Rabu 09.45-12.15 (1) TC104Kelas B Rabu 12.45-15.15
(1) TC105
Chairman Kelas Kelas A Kelas B
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Our class SC(C) & SC(D)
JadwalKelas C Rabu, 09.45-12.15 (2) TC105Kelas D Rabu,
15.30-18.00 (2) TC107
Chairman Kelas Kelas C Kelas D
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Our class SC(C) & SC(D)
E-Learning (to be informed) Lab/Praktikum (to be informed)
14 pertemuan, UTS, UAS = 16pertemuan Tidak masuk 20%, (0,2*14=
3x saja!)
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Aturan Kuliah
Kelas dimulai tepat waktu No plagiarism (sengaja maupun tidak =
E) Fokus dan konsentrasi penuh pada kuliah Miliki referensi utama
dalam bentuk
hardcopy
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Baca! Kontrak Kuliah Buku Ref. & Penunjang
Make a resume: buat catatan/resume(A4), per Pokok Bahasan
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Week
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Q1 Q2 Q3
Case-Based
Reasoning
Expert System UTS Genetic
Algorithm
Fuzzy Logic &
Inferences
Support
VectorMachines
Integrated
AdvancedSystems
UAS
TA1 TA2 TA3 TA4
Rule based Advanced techniques Combined
Weekly Overview
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Minggu 1: Intro SC
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The AI tree
Applications___________________
Disciplines
(E. Turban, 20011)
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Apa itu AI?
Artificial Intelligence is the synthesis and analysis
ofcomputational agents that act intelligently .
An agent is something that acts in an environment .
An agent acts intelligently if: its actions are appropriate for
its goals and circumstances it is flexible to changing environments
and goals it learns from experience it makes appropriate choices
given perceptual and
computational limitations
(D. Poole & A. Mackworth, 2010)
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Intelligent agent (IA) is an autonomous entity which
observes through sensors and actsupon an environment using
actuators
(i.e. it is an agent) and directs itsactivity towards achieving
goals (i.e. itis rational)
Reference:Russell, Stuart J.; Norvig, Peter (2003), Artificial
Intelligence: A Modern Approach (2nd ed.),Upper Saddle River, New
Jersey: Prentice Hall, ISBN 0-13-790395-2, chpt. 2
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Perscpective On Intelligence
Intelligence can be defined along twoaxis:
1. Thinking v.s. Action2. Humanlike v.s Rationally(Russel &
Norvig, 2009)
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Thinkinghumanly
Thinkingrationally
Actinghumanly Actingrationally
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Contoh
Lampu otomatis
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Goals of AI
Scientific goal : to understand the principles that
makeintelligent behavior possible in natural or
artificialsystems.
analyze natural and artificial agents formulate and test
hypotheses about what it takes to
construct intelligent agents design, build, and experiment with
computational systems
that perform tasks that require intelligence
Engineering goal : design useful, intelligent artifacts .
The analogy between studying flying machines andthinking
machines.
(D. Poole & A. Mackworth, 2010)
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Agents acting in an Environment
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Example agent: robot
Abilities movement, grippers, speech, facialexpressions,. .
.Goals/Preferences deliver food, rescue people, score
goals,explore,. . .Prior Knowledge what is important feature,
categories of
objects, what a sensor tell us,. . .Observations vision, sonar,
sound, speech recognition,gesture recognition,. . .Past experiences
effect of steering, slipperiness, howpeople move,. . .
(D. Poole & A. Mackworth, 2010)
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Example agent: teacher
Abilities present new concept, drill, give test,
explainconcept,. . .Goals/Preferences particular knowledge, skills,
inquisitiveness,
social skills,. . .Prior Knowledge subject material, teaching
strategies,.Observations test results, facial expressions,
errors,focus,. . .Past experiences prior test results, effects of
teachingstrategies, . . .
(D. Poole & A. Mackworth, 2010)
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Example agent: medical doctor
Abilities Goals/Preferences Prior Knowledge Observations Past
experiences
(D. Poole & A. Mackworth, 2010)
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Example agent: Apple Inc.
Abilities ?Goals/Preferences ?Prior Knowledge ?Observations
?Past experiences ?
(D. Poole & A. Mackworth, 2010)
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Reference
1. Efraim Turban, Decision Support and Business
IntelligenceSystem (International Edition), 9th Edition, Pearson
2011(Chapter 12, 13)
2. D . Poole & A. Mackworth. Artificial Intelligence:
Foundationsof Computational Agents. Cambridge University Press
(2010 )
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Machine Learning
Supervised Learning Classification Regression
Unsupervised Learning Clustering (Segmentation) Association
Reinforcement Learning
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Minggu 1: Recap
Week 2: Quiz on CBR (di akhir kuliah, 30menit)
Bahan Book 1. Bab 13.2 13.3
Week 2: TA1 Application Case 13.1
Week 3: TA1 13.8(*) baca Kontrak Kuliah dg. teliti..........
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Discussion Media
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