Artificial Intelligence Introduction (2). What is Artificial Intelligence ? making computers that think? the automation of activities we associate.

Artificial Intelligence

Introduction (2)

What is Artificial Intelligence ?

making computers that think?

the automation of activities we associate with human

thinking, like decision making, learning ... ?

the art of creating machines that perform functions that

require intelligence when performed by people ?

the study of mental faculties through the use of

computational models ?

What is AI?

There are no crisp definitionsQ. What is artificial intelligence? A. It is the science and engineering of

making intelligent machines, especially intelligent computer programs.

Q. what is intelligence? A. Intelligence is the computational part

of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.

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Alan M Turing, Hero Helped to found theoretical CS

– 1936, before digital computers existed Helped to found practical CS

– wartime work decoding Enigma machines– ACE Report, 1946

Helped to found practical AI – first (simulated) chess program

Helped to found theoretical AI …

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What did Turing think? Turing (in 1950) believed that by 2000

– computers available with 128Mbytes storage– programmed so well that interrogators have

only a 70% chance after 5 minutes of being right

“By 2000 the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted”

Turing Test Three rooms contain a person, a

computer, and an interrogator. The interrogator can communicate with

the other two by teleprinter. The interrogator tries to determine which

is the person and which is the machine. The machine tries to fool the interrogator

into believing that it is the person. If the machine succeeds, then we

conclude that the machine can think.

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The Imitation Game Interrogator in one room

– computer in another– person in a third room

From typed responses only (text-only), can interrogator distinguish between person and computer?

If the interrogator often guesses wrong, say the machine is intelligent.

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Can Machines Think? Turing starts by defining machine &

think– Will not use everyday meaning of the

words otherwise we could answer by Gallup poll

– Instead, use a different question closely related, but unambiguous

“I believe the original question to be too meaningless to deserve discussion”

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A sample game Turing suggests some Q & A’s: Q: Please write me a sonnet on the subject of the Forth

Bridge A: Count me out on this one, I never could write poetry Q: Add 34957 to 70764.

– (pause about 30 seconds) A: 105621 Q: Do you play chess? A: Yes Q: I have K at my K1, and no other pieces. You have only K

at K6 and R at R1. It is your move. What do you play?– (pause about 15s)

A: R-R8 mate

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Some Famous Imitation Games

1960s ELIZA– Rogerian psychotherapist

1970s SHRDLU– Blocks world reasoner

1980s NICOLAI– unrestricted discourse

1990s Loebner prize– win $100,000 if you pass the test

“Chinese room” argument [Searle

1980]

Person who knows English but not Chinese sits in room Receives notes in Chinese Has systematic English rule book for how to write new

Chinese characters based on input Chinese characters, returns his notes

– Person=CPU, rule book=AI program, really also need lots of paper (storage)

– Has no understanding of what they mean– But from the outside, the room gives perfectly reasonable

answers in Chinese! Searle’s argument: the room has no intelligence in it!

image from http://www.unc.edu/~prinz/pictures/c-room.gif

Why is AI hard?

Two usual ingredients (for standard AI) Representation

– need to represent our knowledge in computer readable form

Reasoning– need to be able to manipulate knowledge and

derive new knowledge– finding the successful way usually involves

search

Both of these are hard.

Knowledge Representation

It is the problem of capturing in a

formal language suitable for computer

manipulation.

We will study logic as a language for

AI

Representation Language

An AI representation language must :

– Handle qualitative knowledge

– Allow new knowledge to be inferred from

set of facts and rules

Search Problem

Search is a problem-solving technique to

explores successive stages in

problem-solving process.

Search Problem

We need to define a space to search

in to find a problem solution

To successfully design and implement

search algorithm, we must be able to

analyze and predict its behavior.

State Space Search

One tool to analyze the search space is

to represent it as space graph, so by

use graph theory we analyze the

problem and solution of it.

Graph TheoryA graph consists of a set of nodes and a

set of arcs or links connecting pairs of nodes.

Island1 Island2

River1

River2

Graph structure Nodes = {a, b, c, d, e} Arcs = {(a,b), (a,d), (b,c),….}

a

db

e

c

Tree A tree is a graph in which two nodes

have at most one path between them. The tree has a root.

a

b c d

e f g h i j

Space representation

In the space representation of a

problem, the nodes of a graph

correspond to partial problem solution

states and arcs correspond to steps in

a problem-solving process

Example

Let the game of Tic-Tac-toe

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Strategies for search

The strategies for state space search

are: Data-driven and goal-driven

search

Data-Driven search

It is called forward chaining

The problem solver begins with the

given facts and a set of legal moves or

rules for changing state to arrive to the

goal.

Goal-Driven Search

Take the goal that we want to solve

and see what rules or legal moves

could be used to generate this goal.

So we move backward.

Search Implementation In both types of moving search, we

must find the path from start state to a goal.

We use goal-driven search if– The goal is given in the problem– There exist a large number of rules– Problem data are not given

Search Implementation

The data-driven search is used if– All or most data are given– There are a large number of potential

goals– It is difficult to form a goal