Introduction to AIIntroduction to AI

Russell and Norvig:

Chapters 1 and 2

Introduction to AI 2

Found on the Web …Found on the Web …

AI is the reproduction of the methods of human reasoning or intuitionUsing computational models to simulate intelligent (human) behavior and processesAI is the study of mental faculties through the use computational methods

Intelligent behavior

Humans

Computer

Introduction to AI 3

I personally think that AI started as a rebellion against some form of establishment telling us “Computers cannot perform certain tasks requiring intelligence”For example, for many years AI researchers have regarded computational complexity theory as irrelevant to their field. They eventually had to reckon with it, but in the meantime computational complexity had also changed a lot.

Introduction to AI 4

What is AI?What is AI?

Discipline that systematizes and automates intellectual tasks to create machines that:

Act like humans Act rationally

Think like humans

Think rationally

Introduction to AI 5

Act Like HumansAct Like Humans

AI is the art of creating machines that perform functions that require intelligence when performed by humansMethodology: Take an intellectual task at which people are better and make a computer do itTuring test

•Prove a theorem•Play chess•Plan a surgical operation•Diagnose a disease•Navigate in a building

Introduction to AI 6

ChessChess

Name: Garry KasparovTitle: World Chess ChampionCrime: Valued greed over common sense

Humans are still better at making up excuses.

© Jonathan Schaeffer

Introduction to AI 7

Perspective on Chess: ProPerspective on Chess: Pro

“Saying Deep Blue doesn’t really think about chess is like saying an airplane doesn't really fly because it doesn't flap its wings”

Drew McDermott

© Jonathan Schaeffer

Introduction to AI 8

나비가 나는 이유 ?

나비는 유체역학적으로 날 수가 없다 . 그러나 나비는 그 사실을 모르기 때문에 날 수 있다 .나비는 유체역학적으로 날기에 부적합하다 . 제비는 적합하다 . 그러나 나비는 제비 못지않게 잘 번성하고 있다 . 나비처럼 나는 것도 이유가 있다 . 어떤 나비는 미국에서 호주까지 날아가기도 한다 . 흉내보다는 같은 기능을 하면 충분 !!

두 발로 걷는 로봇 ? 지네 같은 로봇 ?

Introduction to AI 9

Perspective on Chess: Perspective on Chess: ConCon

“Chess is the Drosophila of artificial intelligence. However, computer chess has developed much as genetics might have if the geneticists had concentrated their efforts starting in 1910 on breeding racing Drosophila. We would have some science, but mainly we would have very fast fruit flies.”

John McCarthy

© Jonathan Schaeffer

Introduction to AI 10

Think Like HumansThink Like Humans

How the computer performs functions does matterComparison of the traces of the reasoning stepsCognitive science testable theories of the workings of the human mind

But, do we want to duplicate human imperfections?

Introduction to AI 11

Think/Act RationallyThink/Act Rationally

Always make the best decision given what is available (knowledge, time, resources)A performance measure is required 객관적인가 ? 청소로봇 , 보상 (reward) 디자이너의 평가기준 ? 자원의 제한이 있을 때 ?

Perfect knowledge, unlimited resources logical reasoningImperfect knowledge, limited resources (limited) rationality

•Connection to economics, operational research, and control theory•But ignores role of consciousness, emotions, fear of dying on intelligence

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Bits of HistoryBits of History

1956: The name “Artificial Intelligence” was coined. (Would “computational rationality” have been better?)Early period (50’s to late 60’s): Basic principles and generality General problem solving Theorem proving Games Formal calculus

Introduction to AI 13

Bits of HistoryBits of History1969-1971: Shakey the robot (Fikes, Hart, Nilsson) Logic-based planning (STRIPS)Motion planning (visibility graph)Inductive learning (PLANEX)Computer vision

Introduction to AI 14

Bits of HistoryBits of History

Knowledge-is-Power period (late 60’s to mid 80’s): Focus on narrow tasks require

expertise Encoding of expertise in rule form:

If: the car has off-highway tires and4-wheel drive andhigh ground clearance

Then: the car can traverse difficult terrain (0.8) Knowledge engineering 5th generation computer project CYC system (Lenat)

Introduction to AI 15

Bits of HistoryBits of History

AI becomes an industry (80’s – present): Expert systems: Digital Equipment,

Teknowledge, Intellicorp, Du Pont, oil industry, …

Lisp machines: LMI, Symbolics, … Constraint programming: ILOG Robotics: Machine Intelligence

Corporation, Adept, GMF (Fanuc), ABB, … Speech understanding

Introduction to AI 16

Bits of HistoryBits of History

The return of neural networks, genetic algorithms, and artificial life (80’s – 90’s)Increased connection with economics, operational research, and control theory (90’s – present)AI becomes less philosophical, more technical and mathematically oriented

Introduction to AI 17

Predictions and Reality … Predictions and Reality … (1/3)(1/3)

In the 60’s, a famous AI professor from MIT said: “At the end of the summer, we will have developed an electronic eye”As of 2002, there is still no general computer vision system capable of understanding complex dynamic scenesBut computer systems routinely perform road traffic monitoring, facial recognition, some medical image analysis, part inspection, etc…

Introduction to AI 18

Predictions and Reality … Predictions and Reality … (2/3)(2/3)

In 1958, Herbert Simon (CMU) predicted that within 10 years a computer would be Chess championThis prediction became true in 1998Today, computers have won over world champions in several games, including Checkers, Othello, and Chess, but still do not do well in Go

Introduction to AI 19

Predictions and Reality … Predictions and Reality … (3/3)(3/3)

In the 70’s, many believed that computer-controlled robots would soon be everywhere from manufacturing plants to homeToday, some industries (automobile, electronics) are highly robotized, but home robots are still a thing of the futureBut robots have rolled on Mars, others are performing brain and heart surgery, and humanoid robots are operational and available for rent (see: http://world.honda.com/news/2001/c011112.html)

Introduction to AI 20

Mistakes …Mistakes …

Often, the potential of a new field is over-estimated in its early age, but under-estimated over the longer termAI proponents have over-estimated the need for smart software, and under-estimated the feasibility and potential of large software systems based on massive coding effort

Introduction to AI 21

생물학적으로 본 인간

인류의 진화뇌의 크기언어의 사용도구의 사용직립보행 사회생활 예측에 의한 행동 ?

Introduction to AI 22

본능인가 학습인가 ?

각인언어능력모국어

Introduction to AI 23

인간과 다른 유인원

Introduction to AI 24

뇌의 진화과정Sahelanthropus tchadensis

600 만 500 만 400 만 300 만 200 만 100 만

원인류

오스트랄로 피테쿠스

400-500 cc

Homo habilus(handy man)600-800 cc

Homo erectus

800-1200 cc

Homo sapiens

1300-1700 cc

뇌용적 :

유럽인

아프리카인

동아시아인

호주 원주민

네안데르탈 인

Introduction to AI 25

뇌의 용량

Introduction to AI 26

인류의 진화

Introduction to AI 27

Human Brain

Chimpanzee Brain

Introduction to AI 28

보복전쟁 원인은 진화덜된 두뇌탓

미국 폴 로스코 교수 주장

미국 과학진흥협회 (AAAS) annual meeting

“ 핵 기술을 보유한 인간의 두뇌가 여전히 석기 시대 수준에 머물러 있다”

“ 자신뿐 아니라 자기 종족까지 죽이는 ‘복수’ 행위는 정상적인 진화의 산물이 아니다”

Introduction to AI 29

일란성 쌍둥이 연구 건선

우울병

정신분열병

IQ

신경증

당뇨병

천식

심장병

암

다발성 경화증

강한 유전자 영향

약한 유전자 영향

Introduction to AI 30

언어의 진화

Introduction to AI 31Evolutionary leap. One of these primates is able to talk about what he's seeing; the other isn't.

'Speech Gene' Tied to Modern Humans

FOXP2 gene

- first identified by Monaco’s group at Oxford University (Science 2001)

- 715 amino acids, two amino acid mutation in human lineage since 6 million years ago – fixed at 120,000 – 200,000 years ago (Svante Paabo, Nature 2002)

Introduction to AI 32

언어를 관할하는 FOXP2 유전자의 돌연변이

Introduction to AI 33

유전자의 영향

IQ ( 어머니와 아들 )모성애 ( 아버지와 딸 (NAST))

• 3 번 exon(long) : 창조성 , 탐구성 , 스릴 추구• 3 번 exon(short) : 완고 , 융통성 결여• D4 장애 : 정신분열병

도파민 D4 수용체 (11 번 염색체 )

Introduction to AI 34

아인슈타인의 뇌

Introduction to AI 35

Richard J. Davidson et al., Science 2000

( 폭력과 전전두엽 장애 – 세로토닌 신경계 장애 )

연쇄살인자 – 세로토닌 장애 – 책임문제

정신분열병 – 도파민 장애 – 살인 - 면죄부

폭력의 생물학

Introduction to AI 36

전극의 전체 레이아웃 기록전극 부분의 확대도

생체 전자 공학

배양을 시작한 직후의 신경세포종의 모습 배양 후 2 일이 경과한 신경세포종의 모습

Introduction to AI 37

생체전자공학

뇌에 기계 또는 전자적 연결 눈 수술 귀 수술

Introduction to AI 38

transgenic mice overexpressing the NR2B subunit of the NMDA

receptor

뇌의 특정 유전자 과도 발현하는 형질 전환 마우스 제조 (smart mice)

T. V. P. BLISS et al., Nature,1999

Introduction to AI 39

최초의 유전자 치료술 ( 중증 복합 면역결핍증 , SCID)

Introduction to AI 40

Mistakes …Mistakes …

Often, the potential of a new field is over-estimated in its early age, but under-estimated over the longer termWhat about Bio-informatics?

줄기세포의 치료효과 쥐의 실험에 의하면 척추장애를 완벽히 치료 그러나 저항력이 없는 쥐에서는 바로 암으로 전이 , 있을

때에도 3개월 뒤면 암으로 전이

? 탄소 큐브 ( 나노기술 ) 완벽한 새로운 물질 인류는 경험하지 못했으며 , 가장 무서운 발암물질 ?

핵 , 유전자변형 ???

Introduction to AI 41

Intelligent Agent

인간의 능력을 대신할 수 있는 부분적 : 걷는 ? 말하는 ? 판단하는 ? 인간과 같은 ?

지능적 능력을 가진 인간과 다른 형태의 ? 말하는 개 ? 말하는 앵무새 ? 덧셈을 하는 개 ?

비교 : SOAP, Service-oriented approach, XML, Context, …

Introduction to AI 42

Notion of an AgentNotion of an Agent

environmentagent

?

sensors

actuators laser range finder

sonarstouch sensors

Introduction to AI 43

Notion of an AgentNotion of an Agent

environmentagent

?

sensors

actuators

Introduction to AI 44

Notion of an AgentNotion of an Agent

environmentagent

?

sensors

actuators

•Locality of sensors/actuators•Imperfect modeling•Time/resource constraints•Sequential interaction•Multi-agent worlds

Introduction to AI 45

단순화한 인공지능 문제

에이전트의 조건 성공을 평가한 판단기준 환경 또는 응용영역에 대한 사전지식 Agent 가 행할 수 있는 행위 현재까지 한 행위와 결과에 대한 인식

문제의 어려움 Fully observable vs. Partially observable Deterministic vs. Stochastic Episodic vs. Sequential Static vs. Dynamic Discrete vs. Continuous Single agent vs. Multiagent

Introduction to AI 46

Example: Tracking a Example: Tracking a TargetTarget

targetrobot

• The robot must keep the target in view• The target’s trajectory is not known in advance• The robot may not know all the obstacles in advance• Fast decision is required