Lecture 1: Introduction Heshaam Faili [email protected] University of Tehran What is AI? Foundations of AI The History of AI State of the Art.

Lecture 1: Introduction

Heshaam [email protected]

University of Tehran

What is AI?

Foundations of AIThe History of AIState of the Art

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Definitions of AI

Develop programs/systems that perform/act like humans

Develop programs/systems that perform/act rationally

Understand human intelligence Formalize the laws of thought and

action

INTELLIGENT AGENTS

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HUMAN COMPUTER/

HUMAN- types in questions- receives answers on screen

- processes questions- returns answers

What is AI?

Acting Humanly:The Turing Test

If the human cannot tell if it is a computer or a human, the program exhibits intelligence

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Turing Test Simple Turing test involve

NLP Knowledge representation Automated reasoning Machine learning

To enhance should have Computer vision robotics

AI researchers have devoted little effort to passing the Turing test,

believing that it is more important to study the underlying principles of in- intelligence than to

duplicate an exemplar. The quest for "artificial flight" succeeded when the

Wright brothers and others stopped imitating birds and learned about

aerodynamics.

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Thinking humanly Cognitive modeling

Computer model together experimental technique from psychology

We will not attempt to describe what is known of human cognition

We will occasionally comment on similarities or differences between AI techniques and human cognition.

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Thinking rationally

The "laws of thought" approach Aristotle’s “right thinking”

Pattern for argument structure yield correct conclusion

E.g : "Socrates is a man; all men are mortal; therefore, Socrates is mortal."

Logic

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Acting rationally An agent is just something that acts computer agents are expected to

have other attributes that distinguish them from mere "programs,

A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome.

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Examples of task for AI Play games

tic-tac-toe, chess, backgammon, poker Process natural language

control tower conversation, stock market briefs

Industrial applications plant diagnostics, plan for manufacturing

Expert-level performance molecular biology, computer configuration

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Why is AI different than conventional programming?

Strive for GENERALITY EXTENSIBILITY

Capture rational deduction patterns Tackle problems with no algorithmic

solution Represent and manipulate KNOWLEDGE,

rather than DATA A new set of representation and

programming techniques: HEURISTICS

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Example: TIC-TAC-TOE

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Program 1: hard wired

Code a table of all possible board positions and the transitions between them (state diagram)

Given a position, look in the table for the next move and return

Properties: time efficient, requires lots of storage not extensible: requires a table for other

games

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Program 2: less hard wired

Use procedures designed for the game: try to place two marks in a row if opponent has two marks in a row, place

mark in third space

Pattern matching to recognize board positions

Can encode different playing strategies Better space efficiency, less time

efficiency Still game-dependent

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Program 3: AI-like

Represent the state of the game: current board position next legal positions

Use an evaluation function: Rate the next move according to how

likely it will lead to a win look-ahead of possible oponent moves

More general because it embodies a general strategy.

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Foundations of AI

Philosophy: Aristotle: the first one worked on I: way

of thinking mechanistic views: of behavior materialism or dualism: of mind Empiricism: for generate a knowledge Logical Positivism: all knowledge can

be connected to gather logically

•Can formal rules be used to draw valid conclusions? •How does the mental mind arise from a physical

brain? • Where does knowledge come from? • How does knowledge lead to action?

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Foundations of AI

Mathematics: algorithms, logic, formalization of mathematics, Incompleteness, NP-completeness, decision theory

•What are the formal rules to draw valid conclusions?

• What can be computed? •How do we reason with uncertain

information?

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Foundations of AI

Psychology: behaviorism, cognitive science.

Linguistics: grammars, syntax and semantics.

Computer Science: computers, software, theory

Others: neuroscience, economics, game theory.

How do humans and animals think and act?

• How does language relate to thought?

• How can we build an efficient computer?

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A brief history of AI (1) Gestation (43-56):

automata theory, neural networks, checkers, theorem proving.

Shannon, Turing, Von Neumann, Newell and Simon, Minsky, McCarthy, Darmouth Workshop.

Great expectations (52-69): computers can do more than arithmetic! Physical symbol system General Problem Solver (GPS), better checkers LISP (LISt Processing language): AI

programming language

birth of AI: 1956

"computational rationally”

"a physical symbol system has the necessary and sufficient means for

general intelligent action."

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A brief history of AI (2) Microworlds: ANALOGY, blocks world

Minsky supervised a series of students who chose limited problems that

appeared to require intelligence to solve.

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A brief history of AI (3) A dose of reality (66-74):

ELIZA: human-like conversation. limitations of neural networks, genetic

algorithms, machine evolution. acting in the real world: robotics.

Knowledge-based systems (69-79): All previous methods are weak methods !! domain focus: experts systems vs. General

Problem Solvers. DENDRAL(in Chemical experiment),

MYCIN(medical), XCON, etc.

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A brief history of AI (4) Commercial AI: the ‘80s boom (80-

90) DEC’s R1 computer configuration program:

saving 40$ million in year many expert systems tools companies

(mostly defunct): Symbolic, Teknowledge, etc.

Japan’s 5th generation project: PROLOG. limited success in autonomous robotics and

vision systems.

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A brief history of AI (5) The 90’s: specialization, quiet

progress neural networks, genetic algorithms probabilistic reasoning and uncertainty learning planning and constraint solving agents autonomous robotics: NAV autonomous

driving van, crater exploration, robot soccer IBM’s Deep Blue beats Kasparov!

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State of the Art Embedded AI: many use AI techniques

without saying it is AI! Credit card approval (American Express) Consumer electronics (fuzzy logic)

Healthy research in many areas: intelligent agents, machine learning, man-machine interfaces, etc.

More integrative view: acting in the real world (robots, self diagnosing machines)

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