Artificial Intelligence
A Brief History
1
Great Expectations
It is not my aim to surprise or shock you
–
but the simplest way I
can summarize is to say that there are now in the world
machines that think, that learn and that create. Moreover, their
ability to do these things is going to increase rapidly until
–
in a
visible future
–
the range of problems they can handle will be
coextensive with the range to which the human mind can be
applied.
We have invented a computer program capable of thinking non

numerically, and thereby solved the venerable mind

body
problem.
Herbert Simon, 1957r.
2
Early Successes
•
L
ogic
Theorist
proved
38
out of
52 t
heorems of Chapter 2 of
Principia
Mathematica
•
Geometry Theorem Prover
proved
theorems too hard for
undegraduate students in mathematics
•
ELIZA
, computer

based psychoterapist helped many
hypochondriacs
•
MYCIN
, an expert system to diagnose blood infections, was
able to perform considerably better than junior doctors
3
Trouble
•
Solutions developed for „microworlds” did not apply in the
real world (computational complexity)
•
Expert systems could not be extended to broader domains
(context)
•
Fiasco of the automatic translation project (context)
–
The spirit is willing but the flesh is weak
–
The vodka is good but the meat is rotten
•
Fiasco of the planning systems (the frame problem)
4
Planning
A
C
B
D
S1
T[On(B,A), S1]
T[Clear(B), S1]
T[Clear(C), S1]
T[Clear(D), S1]
A≠B ≠C ≠D
Plan a sequence of actions
α
=<A1,...,An> such that:
T[On(A,C), Result(
α
,S1]
T[On(D,A), Result(
α
,S1]
5
Planning, cont.
Available actions:
stack: S(x,y)
unstack: U(x,y)
For every atomic action we specify their effects through axioms:
T[Clear(x), S] &
T[Clear(y), S] & x ≠ y →
T[On(x,y), Result(<S(x,y)>, S)]
T[On(x,y), S] & T[Clear(x), S] →
T[Clear(y), Result(<U(x,y)>, S)]
6
Planning, cont.
A
C
B
D
D
C
A
B
D
C
A
B
D
C
A
B
U(B,A)
S(A,C)
S(D,A)
7
Planning

proof
•
T[On(B,A), S1]
•
T[Clear(B), S1]
•
T[Clear(A), S2], where S2=Result(<U(B,A)>,S1)
•
T[Clear(C),S2)
•
T[On(A,C), S3], where S3=Result(<S(A,C)>,S2)
•
Ad hoc solution
–
let’s add frame axioms for the
unstack
action
:
T[Clear(x), S] → T[Clear(x), Result(<U(y,z)>,S)]
false!
8
The Frame Problem (AI version)
How to formalize changes (and lack thereof) in
the world as a result of our actions.
Adding the frame axioms does not solve the problem:
•
It is impractical (we would need millions of such axioms)
•
It is not intuitive (
we
do not do it!)
•
It is often
false
(what should we do when one robot is moving
the blocks while another one is painting them?)
9
Default Logic
Commonsense law of inertia: things stay as they are
unless we have knowledge to the contrary.
γ
β
:
α
Default rule where
α
,
β
,
γ
are formulas.
Once
α
has been established and
β
is consistent with what we
know, we conclude
γ
.
Example: take the generic truth„Birds fly”. In Default Logic we write this as:
flies(x)
flies(x)
:
bird(x)
If we know that Tweety does not fly (because he is an ostrich), the rule will not fire
despite the fact
that Tweety is a bird.
10
Default Logic: theory
E is an extension of <W,D> iff there exist E
0
, E
1
,
E
2
, ... such that:
0
i
i
E
E
}
E
β
,
E
α
D,
γ
β
:
α

{
γ
)
Cn(E
E
i
i
1
i
W
E
0
11
Default Logic: example
Quaker
Pacifist
Nixon
Republican
W={R(nixon), Q(nixon)}
}
P(x)
P(x)
:
R(x)
,
P(x)
P(x)
:
Q(x)
{
D
This theory has two extensions:
)
{P(nixon)}
Cn(W
E
1
P(nixon)})
{
Cn(W
E
2
12
Default Logic: problem
This theory also has two extensions. This time,
however, this does not agree with our intuitions.
Amish
Speaks German
Born in
Pennsylvania
Born in the USA
Hermann
We solved the Frame Problem to face the
problem of relevance.
13
What Next?
17
Path 1: Stay the Course
Projekt CYC
The problem of AI is commonsense knowledge: let’s add it
then!
Goals:
–
30 people are entering data from newspapers, ads,
disctionaries, etc.
–
After 6 years a million assertions have been entered; the goal
was 100 million
–
CYC had its own ontology, representations of causal
relationships and simple rules of relevance
The project came to an end in 1994 r. (after 50 mln $); its
remnants are still around today
EN
CYC
LOPEDIA
18
Path 2: Change the Paradigm
Dreyfus’s criticism: AI’s basic assumptions are wrong!
•
Biological assumption: the brain is a symbol

manipulating device like a digital computer.
•
Psychological assumption: the mind is a symbol

manipulating device like a digital computer.
•
Epistemological assumption: intelligent behavior can
be formalized and thus reproduced by a machine.
•
Ontological assumption: the world consist of
independent, discrete facts.
19
Path 2: cont.
Filozoficzni przodkowie AI (według Dreyfusa):
•
Kartezjusz: wszelkie rozumowanie polega na manipulacji
reprezentacjami symbolicznymi złożonymi z prostych idei
•
Kant: wszelkie pojęcia można zbudować z prostych
elementów przy użyciu reguł
•
Frege: reguły można sfromalizować tak, by używać ich
bez konieczności ich rozumienia lub interpretacji
20
Path 2 cont.
•
Mind (intelligence) is:
–
situated in the environment (Heidegger:
In

der

Welt

sein
)
–
embodied (Merleau

Ponty:
le corps propre
)
•
AI Lab at MIT (Rodney Brooks) builds the first robots following these
tenets (e.g.
Big Dog
).
•
Dreyfus’s views are further developed by: Andy Clark, John
Haugeland, Michael Wheeler, Walter Freeman
•
New trends in cognitive science:
embodied cognition, dynamicism,
neurophenomenology, neurodynamics
...
21
Path 3: Change the Goal
•
Distinguish between
strong
and
weak
AI
–
Strong AI: we build machines that really think
–
Weak AI: we build machines that behave
as if
they were thinking
•
We are only interested in the weak AI
–
Even weaker version: we build machines that behave
rationally
•
We stay with the logistic approach
22
Path 3: State of the Art
Which of the following can be done at present?
•
Play a decent game of table tennis
•
Drive safely along a curving mountain road
•
Drive safely along Telegraph Avenue
•
Buy a week’s worth of groceries on the web
•
Buy a week’s worth of groceries at Berkeley Bowl
•
Play a decent game of bridge
•
Discover and prove a new mathematical theorem
•
Design and execute a research program in molecular biology
•
Write an intentionally funny story
•
Give competent legal advice in a specialized area of law
•
Translate spoken English into spoken Swedish in real time
•
Converse successfully with another person for an hour
•
Perform a complex surgical operation
•
Unload any dishwasher and put everything away
23
Path 3: State of the Art
Which of the following can be done at present?
•
Play a decent game of table tennis
•
Drive safely along a curving mountain road
•
Drive safely along Telegraph Avenue
•
Buy a week’s worth of groceries on the web
•
Buy a week’s worth of groceries at Berkeley Bowl
•
Play a decent game of bridge
•
Discover and prove a new mathematical theorem
•
Design and execute a research program in molecular biology
•
Write an intentionally funny story
•
Give competent legal advice in a specialized area of law
•
Translate spoken English into spoken Swedish in real time
•
Converse successfully with another person for an hour
•
Perform a complex surgical operation
•
Unload any dishwasher and put everything away
24
AI and Cognitive Science
AI 50 years ago
Cognitive
Science
AI today
Logic
Thinking
Acting
Rationally
Humanly
The central question in the discussion about the methodology of AI : can AI learn from
Cognitive Science?
Has aeronautics learn anything from ornitology?
25
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