1

I - Quick lookI - Quick look

2V 3.19

1 - Artificial Intelligence ? (a few definitions)1 - Artificial Intelligence ? (a few definitions)1.1 - An ‘artificial intelligence’ ? 1.1 - An ‘artificial intelligence’ ?

Artificial + Intelligence• Intelligence : ability to understand and reason (like a human being does)• Artificial : man made

Thus, Artificial Intelligence or “A.I.” is the study of cognitive phenomenon (dealing with knowledge) made by a machine (real or not) in order to bring it as near as possible to those used by man .

3V 3.19

1.2 - ‘Larousse’ dictionary1.2 - ‘Larousse’ dictionary

Set of theories and techniques used in order to build machines able to simulate human intelligence in a definite domain.

4V 3.19

1.3 - Laurière (Jean-Louis)1.3 - Laurière (Jean-Louis)

Every problem for which no algorithmic solution is known, a priori comes under AI.An algorithm is :“an ordered sequence of operations, that can run on a computer and gives a solution in a finite time”.Examples :

• playing chess• sumarising a text, translating it.• diagnose an illness• recognising someone’s face• proving a theorem• learning…

The object of AI is to rebuild with artificial means, for the most part computers, reasonings and intelligent actions.

5V 3.19

1.4 - As a conclusion…1.4 - As a conclusion…

One of the the aims of AI is to describe quite precisely human reasoning to make it work on a computer.

But replacing human reasoning is not enough, the reasoning has to be ‘complex’ (i.e. not evident).

Figure 1.1 - A trivial problem

ii=1

n

∑

n x (n-1)2

Add the n first

integers

beginS := 0;for i := 1 to

n doS := S +

i;

S := (n * succ(n))/2

6V 3.19

1.5 - Kind of summary1.5 - Kind of summary

From a ‘problem’ point of view : AI deals with ‘intelligent’, complex problems difficult or impossible to solve with current means.

From a ‘tool’ point of view : AI wants to make more human a computer (in its input/output, reasoning…).

7V 3.19

1.6 - By the way,1.6 - By the way,what is ‘intelligence’ ?what is ‘intelligence’ ?

Figure 1.2 - Intelligence ?

Ability to live together

Ability to find the reason of the break-down

Chimpanzee Diagnose Expert System

8V 3.19

2 - History2 - History2.1 - Birth2.1 - Birth

August 1956, Darmouth College conference (Hanover, New Hampshire), John MacCarthy gives the science its name : ‘Artificial Intelligence’ and propose, in presence of Minsky, Newell, Simon, Shanon...

“to study if all that is behind ‘intelligence’

could be describe precisely enough

to be fulfilled by a machine”.

9V 3.19

2.2 - AI & Logic2.2 - AI & Logic

First programs : “Logic Theorist” (theorem proving) and a chess game both from Newell & Simon. (Lisp too !)

Logic, Mathematics and AI are deeply linked (paradoxes). Since antiquity (Aristotle’s syllogism) man has been interested in “clever” machines.

10V 3.19

2.3 - Thinking machines2.3 - Thinking machinesCharles Babbage andCharles Babbage andAdelaïde Augusta Baroness of LovelaceAdelaïde Augusta Baroness of Lovelace

Babbage’s analytic machine, 1842 (fictive machine) was able, according to Ada Baroness of Lovelace, to alter itself ; this machine had a warehouse (memory) and a factory (processor)

Pascal or Leibniz machines (XVII th C.) only computed fixed operations : additions, multiplications.

11V 3.19

2.4 - Alan Turing 2.4 - Alan Turing (Universal machine 1936)(Universal machine 1936)

English mathematician dead in 1954. During the second world war, he achieved the machine he had first conceived in 1936, a machine which decodes, in less than a day, German messages coded by Enigma.

A TUM can execute, hopelessly slowly, any of our present programs.

12V 3.19

2.5 - Four decades of AI2.5 - Four decades of AI

The 50s : Birth of AI.

• Scientists’ exaggerated optimism.

• Under estimation of problems leading to failure especially in games (chess) and speech recognition fields.

The 60s : Real start.

• Heuristic research algorithms (introduction of an intelligent function in an algorithm)

• GPS• MACSYMA (formal calculation in mathematics)

13V 3.19

2.5 - Four decades of AI (2)2.5 - Four decades of AI (2)

The 70s : Explosions of studies

• Foundations of AI in :• Representation of knowledge• Expert systems• Natural language• Advanced robotics

The 80s : Entry of AI in economy

• AI goes from research towards industry. Research goes on and even increases.

The 90s : Diversification

• AI techniques, such as fuzzy logic, object representation, natural language or forward chaining, are integrated in classical tools (data bases, automaton, controllers, help systems…)

14V 3.19

3 - Applications fields3 - Applications fields

We are now going now to try to enumerate, rather schematically, applications fields of AI. We will give, for every scope, application examples, and the names of artificial intelligence tool used.

We will first deal with points what won’t be dealt more deeply later in this course.

15V 3.19

3.1 - To be seen later3.1 - To be seen later

Knowledge representation (in II-A) Problem resolution (in IV) Reasoning (in II-B) Problem where the methodology of resolution is impossible to get (in III)

16V 3.19

3.2 -Human reasoning vs computer reasoning3.2 -Human reasoning vs computer reasoning

PsychologistsParadox : Complex tasks are easy to explain, whereas innate tasks can be impossible to explain.

Play chess Talk with someoneInvert a matrix Recognize a face

Make a diagnosis Be conscious of oneself being… Help device in Santa-Anna Loan in a bank

A machine can tell if a loan is authorized after having asked questions to a customer.

Turing test (slow and with errors ?) Problem of the limit of human intelligence, what does the machine have to do

when it can do better than man ?

17V 3.19

3.3 - Natural language3.3 - Natural language

Kind of problems

• Sentence interpretation and generation in a man-computer dialog context.

• In natural language, word for word translation never works.

• To understand (and translate) a sentence we need to take different language dimensions :

• The lexicon (dictionary)• The syntax (language grammar)• The semantics (meaning)• The pragmatic (social context)

18V 3.19

Total RecallTotal Recall

19V 3.19

3.3 - Natural language (2)3.3 - Natural language (2)

Does a unique representation of something in the brain exist whatever the tongue and the form of the sentence may be ?

Technical tools

• Prolog language, semantic networks, algorithmic languages… Example

• www.pagesjaunes.fr (very few AI)A human being knows that he can buy bread at the baker’s, a computer doesn’t, pagejaunes does. The machine proceeds toward human. The phone data base has a phonetic index MEUZIN —> MESSIN, MORROIT -> ...

• Since 93 : Natural language : “I want to hire a car”, “I want to get rid of my mother-in-law”.

20V 3.19

3.4 - Speech processing3.4 - Speech processing

Purpose

• This point covers two aspects : speech recognition (you speak to a machine and are understood) and speech synthesis (the machine talks).

• Problems• Noise in the analogic voice signal• Various speakers• Continuous speech

• There are tools (via voice, kid games…) but they do not usually go further than half the semantic level.

• Technical tools• Neural networks, algorithms, morphological models

21V 3.19

3.4 - Speech processing (2)3.4 - Speech processing (2)

Figure 1.3 - The whole process of a natural dialog between man and machine

"Do you have a watch ?"

"It is noon"

DAC + Speaker

Micro + ADC Speech recognition

Understanding

Reasoning

Sentence generation

Speech generation

22V 3.19

3.5 - Robotics3.5 - Robotics

“Robot” : Czech word meaning “Forced work”. The robot is closer to human because we physically see a result of its reasoning. It frightens us a little : What if this intelligence would dominate our ? (cf. Asimov,

Huxley, Wells, Orwell…). In industry, robots can for example look at their environment and take a decision

in terms of task scheduling.

23V 3.19

3.6 - Vision, pattern recognition3.6 - Vision, pattern recognition

Kind of problem

• Nowadays, a whole scene (a photo for example) is quite impossible to understand, on the contrary, things are done in

• Printed or hand made type recognition,• Cell recognition in a microscopic picture,• Specific areas in a satellite picture,• Numbers or defaults on parts that comes before a camera,• …

24V 3.19

3.6 - Vision, pattern recognition (2)3.6 - Vision, pattern recognition (2)

Figure 1.4 - A french zip code

Technical tools• Algorithms or neural networks.

25V 3.19

3.7 - Other : Logic problems3.7 - Other : Logic problems

Prolog (PROgrammation LOGique) created by Alain Colmerauer in 1972 in Marseille-Luminy university (France).

Prolog is a declarative language, it uses Robinson’s resolution principal on Horn clauses.

Example• father (john, peter).• father (andrew, john).• father (john, michael).

Use • father (X, peter) ?• father (john, X) ?• father (john, andrew) ?

Andrew

John

Michael Peter

26V 3.19

3.7 - Other : Logic problems (2)3.7 - Other : Logic problems (2)

Then new pieces of information are added (they are methods but they use the same syntax as data) : grandfather (X, Y) :- father (X, A),

father (A, Y).

brother (X, Y) :- father (A, X),father (A, Y).

Second use• grandfather (andrew, peter) ?• brother (peter, michael) ?• grandfather (X, michael) ?• grandfather (X, Y) ?

27V 3.19

3.8 - Other : Symbolic problems3.8 - Other : Symbolic problems

Symbols manipulation. We do not study a number for what it is but rather for what it stands for.

A unique syntax to represent both data and program : lists

• Code = Data = Symbols (represented by lists) Language : Lisp (1958, John MacCarthy) Example : derived function in Lisp

(de derived (expr)(cond ((= expr constante) 0)

((= expr var) 1)((= (car expr) +) (+(derived (cadr expr))

(derived (caddr expr))))

((= (car expr) x) ( ... u’v + uv’ ...))...))

28V 3.19

3.9 - Other : Mixed problems3.9 - Other : Mixed problems

When there is a part of things that are known, and another part that is not. In a process for example, it can be useful to get help from other tools :

• “neuroAgent” technology

• Case based reasoning (CBR)

• Induction

• …

29V 3.19

Two ways...Two ways...

For a computer to deal with intelligent functions, two different ways exist

• A connexionist approach

• A cognitive approach

30V 3.19

Cognitive or symbolic approachCognitive or symbolic approach

31V 3.19

Connexionist or neural approachConnexionist or neural approach