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Artificial Intelligence A Brief History 1

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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

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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 ELIZA MYCIN, an expert system to diagnose blood infections, was able to perform considerably better than junior doctors 3

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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) 4

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Planning AC 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= such that: T[On(A,C), Result(α,S1] T[On(D,A), Result(α,S1] 5

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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)] T[On(x,y), S] & T[Clear(x), S] → T[Clear(y), Result(, S)] 6

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Planning, cont. AC B DDCAB DC A B D C A B U(B,A) S(A,C) S(D,A) 7

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Planning - proof T[On(B,A), S1] T[Clear(B), S1] T[Clear(A), S2], where S2=Result(,S1) T[Clear(C),S2) T[On(A,C), S3], where S3=Result(,S2) Ad hoc solution – let’s add frame axioms for the unstack action: T[Clear(x), S] → T[Clear(x), Result(,S)] false! 8

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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?) 9

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

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Default Logic: theory E is an extension of iff there exist E 0, E 1, E 2,... such that: 11

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Default Logic: example Quaker Pacifist Nixon Republican W={R(nixon), Q(nixon)} This theory has two extensions: 12

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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 German Born in Pennsylvania Born in the USA Hermann We solved the Frame Problem to face the problem of relevance. 13

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The Frame Problem in Philosophy: Dennett Odpowiednikiem problemu ramy w AI jest zagadka myślenia potocznego – Wydaje się, że większość naszych działań nie jest planowana – Na pewno nasza cała wiedza nie jest reprezentowana w postaci zdań (pojemność i szybkość mózgu) – Niemal zawsze rozumujemy używając zastrzeżenia ceteris paribus – Potrafimy bez trudu rozróżnić co jest, a co nie jest istotne dla realizacji naszych działań w danej sytuacji 14

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The Epistemological Frame Problem Jak opisać istotność w postaci zdań logiki, gdy istotność jest holistyczna, otwarta i wrażliwa na kontekst? The Computational Frame Problem Jak ograniczyć proces rozumowania do tego co istotne, gdy istotność jest holistyczna, otwarta i wrażliwa na kontekst? 15

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The Frame Problem in Philosophy: Fodor The Metaphysical Frame Problem: Zdroworozsądkowe prawo inercji uzasadnione jest tylko w kontekście właściwej ontologii. Jaka to ontologia? Ontologia z pojęciami takimi jak ziebieski i fridgeon falsyfikuje zdroworozsądkowe prawo inercji. W tym sensie Problem Ramy to jescze jedno oblicze problemu indukcji. 16

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What Next? 17

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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 ENCYCLOPEDIA 18

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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. 19

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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 20

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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).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

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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 22

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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 23

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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 24

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AI and Cognitive Science AI 50 years agoCognitive Science AI todayLogic 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? 25

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