What Is Artificial Intelligence (AI)? (1/2)
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Introduction
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Chapter 1
Artificial Intelligence Course
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
What Is Artificial Intelligence (AI)? (2/2)
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Develop programs that respond flexibly in
situations that were not specifically
House-cleaning robots
Perceive its surroundings
Navigate on the floor
Respond to events
Decide what to do next
Space exploration
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Synonyms of AI: machine intelligence
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
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Branch of computer science that is concerned
with the automation of intelligent behavior.
Design and study of computer programs that
behave intelligently
Study of how to make computers do things at
which, at the moment, people are better.
Designing computer programs to make computers
smarter.
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
History of AI
Symbolic AI
Subsymbolic AI
1943: Production rules
1956: “Artificial Intelligence”
1958: LISP AI language
1965: Resolution theorem
proving
1943: McCulloch-Pitt’s neurons
1959: Perceptron
1965: Cybernetics
1966: Simulated evolution
1966: Self-reproducing automata
1970: PROLOG language
1971: STRIPS planner
1973: MYCIN expert system
1982-92: Fifth generation
computer systems project
1986: Society of mind
1975: Genetic algorithm
1982: Neural networks
1986: Connectionism
1987: Artificial life
1994: Intelligent agents
1992: Genetic programming
1994: DNA computing
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
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Comparison of AI with Conventional
Programming
a. primarily symbolic
Making machines more useful
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Understanding intelligence
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Engineering
Artificial Intelligence
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Conventional computer
programming
a'. algorithmic
(solutions steps explicit)
b'. primarily numeric
b. heuristic search
(solution steps implicit)
c'. information and control
c. control structure usually
integrated together
separate from domain knowledge
difficult to modify
d'.
d. usually easy to modify,
update and enlarge
e'. correct answers required
e. some incorrect answers
often tolerable
f'. best possible solution
f. satisfactory answers usually
usually sought
acceptable
AI Lecture
Notes
(C)1999 SNU Dept. of Computer Engineering
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
1.1 AI in Practice
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Examples of AI Systems
Language translation systems
Air traffic control systems
Supervisory systems (intelligent buildings)
Automated personal assistants(softbots)
Intelligent highways
Robots for hazardous conditions
Medical diagnosis
Factory automation
Finance and business
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
AI in Practice: Space Exploration
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The camera image was taken by
NASA’s Viking 1 Orbiter spacecraft
while searching for a landing site for
the Viking 2 Lander.
AI Lecture Notes
AI in Practice
A prototype mobile robot
designed by researchers at
NASA’s Jet Propulsion Lab for
exploring the Martian surface
(C)1999 SNU Dept. of Computer Engineering
AI in Practice
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
1.2 AI Theory
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Key elements for realizing AI
Representation
Reasoning
Planning
Learning
Examples of AI Theory
Inferring structure from motion in machine vision
Finding consistent hypothesis in learning
Probabilistic inference in diagnostic reasoning
Search in automated planning
Parsing sentences in language understanding
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
1.3 Identifying and Measuring
Intelligence
1.4 Computational Theories of
Behavior
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Turing test: an intelligence test for computers
Representation (Figs. 1.2 and 1.3):
A formal system or set of mathematical conventions by
which the types of information that play a role in the
theory are made explicit.
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Syntax and semantics
Representation
= notation (syntax) + denotation (semantics)
+ computation
Syntax: checks well-formedness
Semantics: assigns true or false
e.g.: "New York is the closest city to Boston"
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
Representations (Examples #1)
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Fig 1.2: Schematic description of a computational theory
concerned with processing camera images to produce
graphical representations of polyhedral objects
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
Representations (Examples #2)
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Fig 1.3: Alternative representations for a system of roads
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
1.5 Automated Reasoning (1/3)
1.5 Automated Reasoning (2/3)
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Automated reasoning: output conclusions from worldknowledge representation
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Inference and symbolic manipulation
Knowledge of physics: representation of problems in
mathematical symbols.
Knowledge of calculus: manipulation of symbols by
rules for integration and differentiation (= inference)
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
1.5 Automated Reasoning (3/3)
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Combinatorial problems and search:
Many AI problems involve many separate decisions
that tend to depend on one another in complicated ways
⇒ search techniques
Complexity and expressivity
O(n) vs. NP-complete problems
First-order predicate logic
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering
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Representing common-sense knowledge
e.g: John is at home. He has to drive
40 kms to get to work. He obeys the
80-km-per-hour speed limit.
⇒ It will take John at least a half hour to get to work.
Rules of inference used to arrive at this
conclusion:
Universal instantiation
Modus ponens
AI Lecture Notes
(C)1999 SNU Dept. of Computer Engineering