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Artificial IntelligenceA Brief History

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Great ExpectationsIt 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.

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Early Successes• Logic Theorist proved 38 out of 52 theorems 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

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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)

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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]

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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)]

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Planning, cont.

A C

B

D DCAB

DC

A

B

D

C

A

B

U(B,A)

S(A,C)

S(D,A)

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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!

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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?)

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Default LogicCommonsense law of inertia: things stay as they are unless we have knowledge to the contrary.

γβ: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 firedespite the fact that Tweety is a bird.

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Default Logic: theory

E is an extension of <W,D> iff there exist E0, E1, E2, ... such that:

0i

iE E

}Eβ,EαD,γβ:α|{γ)Cn(E E ii1i

WE0

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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(WE1

P(nixon)}){Cn(WE2

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Default Logic: problem

This theory also has two extensions. This time, however, this does not agree with our intuitions.

Amish

Speaks GermanBorn in Pennsylvania

Born in the USA

Hermann

We solved the Frame Problem to face the problem of relevance.

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What Next?

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Path 1: Stay the CourseProjekt 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

ENCYCLOPEDIA

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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.

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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

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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...

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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

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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

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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

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AI and Cognitive Science

AI 50 years ago Cognitive Science

AI today Logic

ThinkingActing

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?