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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

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									INTRODUCTION TO ARTIFICIAL
      INTELLIGENCE
            Massimo Poesio

 LECTURE 1: Intro to the course, History
                  of AI
        ARTIFICIAL INTELLIGENCE:
              A DEFINITION
• The branch of Computer Science whose aim is
  to develop machines able to display intelligent
  behavior
• Strong AI: developing machines all but
  undistinguishable from human beings
• Weak AI: developing SIMULATIONS of human
  intelligence
AI BY EXAMPLE: GAMES
AI BY EXAMPLE: ROBOCUP
AI BY EXAMPLE: LANGUAGE
AI BY EXAMPLE: LANGUAGE
        A BRIEF HISTORY OF AI
• Forerunners, I: logic and ontologies
• Forerunners, II: mechanical machines / robots
• The beginning of AI: Turing, Dartmouth,
  Games, Search
• The role of Knowledge in Human Intelligence
• The role of Learning
• Modern AI
                 ARISTOTLE
• Aristotle developed the first theory of
  knowledge and reasoning – his ideas
  eventually developed into modern
  – LOGIC
  – ONTOLOGIES
          ARISTOTLE: SYLLOGISM
             (Prior Analytics)
The first attempt to develop a precise method for
reasoning about knowledge: identify VALID REASONING
PATTERNS, or SYLLOGISMS

BARBARA:
A: All animals are mortal
A: All men are animals.
A: Therefore, all men are mortal.

DARII:
A: All students in Fil., Logica & Informatica take Intro to AI
I: Some students in Filosofia take Fil., Logica & Informatica.
I: Therefore, some student in Filosofia takes Intro to AI.
FIRST ONTOLOGIES
      LOGIC: BEYOND ARISTOTLE
• Ramon Lull (13th Century): first mechanical
  devices for automatic reasoning (Lull’s disks)
• Leibniz (17th Century): Encoding for syllogisms
• Boole (19th Century): Boolean Algebra
• Frege (1879): Predicate calculus
BOOLEAN ALGEBRA
FORERUNNERS, 2
                    TURING
• Alan Turing is the father of AI
• He was the first to imagine machines able of
  intelligent behavior
• And devised an intelligence test, the TURING
  TEST: replace the question “Can a machine be
  endowed with intelligence” with the question:
  – Can a machine display such human-like behavior
    to convince a human observer that it is a human
    being?
              DARTMOUTH
• In 1956 a group of researchers including J.
  McCarthy, M. Minsky, C. Shannon, N.
  Rochester organized a workshop at Dartmouth
  to study the possibility of developing machine
  intelligence
 THE BEGINNINGS OF AI (1956-1966)
• Early AI researchers identified intelligence
  with the kind of behavior that would be
  considered intelligent when displayed by a
  human, and tried to develop programs that
  reproduced that behavior
• Examples:
  – Chess
  – Theorem proving
          HEURISTIC SEARCH
• This early research
  focused on the
  development of SEARCH
  ALGORITHMS (A*) that
  would allow computers to
  explore a huge number of
  alternatives very
  efficiently
  THE SUCCESS OF EARLY AI




In 1997, the chess-playing program DEEP BLUE developed by IBM
researchers led by Feng-hsiung Hsu, beat the chess world
champion Gary Kasparov over six games
 EARLY AI RUNS INTO TROUBLE (1966-
                1973)
• Soon however researchers realized that these
  methods could not be applied to all problems
  requiring intelligence, and that there were a
  number of ‘simple’ problems that could not be
  handled with these methods at all
  – Example of the first: machine translation (the
    ALPAC report)
  – Example of the second: natural language, vision
 COMMONSENSE KNOWLEDGE IN
  LANGUAGE UNDERSTANDING

• Winograd (1974):
  – The city council refused the women a
    permit because they feared violence.
  – The city council refused the women a
    permit because they advocated violence
        AI KEY DISCOVERIES, 1

• Performing even apparently simple tasks like
  understanding natural language requires lots
  of knowledge and reasoning
THE ‘KNOWLEDGE YEARS’ (1969-1985)
• Development of knowledge representation
  techniques
• Development of EXPERT SYSTEMS
• Development of knowledge-based techniques
  for
  – Natural Language Understanding
  – Vision
     KNOWLEDGE REPRESENTATION
            METHODS
• Logic is the older formalization of reasoning
• It was natural to think of logic as providing the
  tools to develop theories of knowledge and its
  use in natural language comprehension and
  other tasks
• Great success in developing theorem provers
• But AI researchers quickly realized that the
  form of logic required was not valid deduction
 FROM LOGIC TO AUTOMATED REASONING

• Starting from the ‘50s AI researchers
  developed techniques for automatic theorem
  proving
• These techniques are still being developed
  and have been used to prove non-trivial
  theorems
RESOLUTION THEOREM PROVING

     All Cretans are islanders.
     All islanders are liars.
     Therefore all Cretans are liars.
     ∀X C(X) implies I(X)
     ∀X I(X) implies L(X)
     Therefore, ∀X C(X) implies L(X)
     ¬C(X) ∨ I(X)
     ¬I(Y) ∨ L(Y)

     ¬C(X) ∨ L(Y)
    HIGH PERFORMANCE THEOREM
             PROVING

• There are now a number of very efficient
  theorem provers that can be used to
  demonstrate sophisticated mathematical
  theorems
  – Otter
  – Donner & Blitzen
   THE FOUR-COLOR PROBLEM
• Conjecture: given a plane
  divided in regions, it is
  possible to color the regiones
  in such a way that two
  adjacent regions are always of
  different colors using no more
  than 4 colors
• This conjecture was
  demonstrated by an automatic
  theorem prover in 1997
        AI KEY DISCOVERIES, 2
• Neither commonsense nor ‘expert’ reasoning
  involve only valid inferences from certain
  premisses:
  – Commonsense reasoning often involves jumping
    to plausible conclusions
  – Expert reasoning involves making decisions with
    uncertainty
 COMMONSENSE KNOWLEDGE IN
  LANGUAGE UNDERSTANDING

• Winograd (1974):
  – The city council refused the women a
    permit because they feared violence.
  – The city council refused the women a
    permit because they advocated violence
DIAGNOSTIC REASONING IN EXPERT
           SYSTEMS


  IF patient has loss of weight AND
      anorexia AND
      spleen is palpable AND
     fever
  THEN
     patient MAY have Hodgkin’s disease
     BEYOND VALID INFERENCE
• As a result of discovery number 2, AI
  researchers quickly got involved with
  developing better theories of commonsense
  reasoning and uncertain reasoning
  – Drawing inspiration from psychology (work on
    semantic networks, scripts)
  – Developing new inference formalisms
SOME RESULTS FROM COGNITIVE PSYCHOLOGY:
          SEMANTIC NETWORKS
• Collins & Quillian, 1969: knowledge appears to be
  organized around objects and in a taxonomic way
   – A canary is yellow
   – A canary has feathers
   – A canary eats food
• Haviland & Clark 1974, Sanford & Garrod 1979:
  ‘associated’ knowledge available when concepts are
  mentioned
   – I looked around the house.
   – The lounge was very spacious.
AN EXAMPLE OF SEMANTIC NETWORK
COMMONSENSE REASONING: SCRIPTS
   (SCHANK & ABELSON, 1977)


   John entered the restaurant.
   The menu had changed, so he ordered one
   of the new dishes.
   The bill was very steep.
AN EXAMPLE OF SCRIPT
    ‘KNOWLEDGE-BASED’ SYSTEMS FOR
   LANGUAGE UNDERSTANDING: SHRDLU




Winograd, 1974
                 A DIALOGUE WITH SHRDLU

Person: PICK UP A BIG RED BLOCK.
Computer: OK.
Person: GRASP THE PYRAMID.
Computer: I DON'T UNDERSTAND WHICH PYRAMID YOU MEAN.
Person (changing their mind): FIND A BLOCK WHICH IS TALLER THAN THE ONE YOU
ARE HOLDING AND PUT IT INTO THE BOX.
Computer: BY "IT", I ASSUME YOU MEAN THE BLOCK WHICH IS TALLER THAN THE
ONE I AM HOLDING.
Computer: OK.
Person: WHAT DOES THE BOX CONTAIN?
Computer: THE BLUE PYRAMID AND THE BLUE BLOCK.
Person: WHAT IS THE PYRAMID SUPPORTED BY?
Computer: THE BOX.
Person: HOW MANY BLOCKS ARE NOT IN THE BOX?
Computer: FOUR OF THEM.
Person: IS AT LEAST ONE OF THEM NARROWER THAN THE ONE WHICH I TOLD YOU
TO PICK UP?
Computer: YES, THE RED CUBE.
              EXPERT SYSTEMS
• Beginning with the ’70s, AI researchers
  developed systems that could be used to
  diagnose
  – Illnesses (MYCIN, DXPLAIN)
  – Mechanical problems
     • DELTA-CATS1, General Electric, Bonissone et al 1984
     • IDEA, Centro Ricerche Fiat, ~1993
  – etc
EXPERT SYSTEMS
   A COMMONSENSE ENCYCLOPEDIA:
              CYC
• A project initiated in 1984 by Doug Lenat. The goal: to
  encode all of commonsense knowledge
• Changed the underlying formalism several times.
   – These days: a logic-based representation
• Two versions available:
   – OpenCyc (http://www.opencyc.org/)
      • 50 000 concepts, 300 000 facts
      • Can be downloaded / on the Web
   – ResearchCyc (http://research.cyc.com/)
      • 300 000 concepts, 3 million facts
             KNOWLEDGE IN CYC

"Bill Clinton belongs to the class of US Presidents“

(#$isa #$BillClinton #$UnitedStatesPresident)


“All trees are plants”
(#$genls #$Tree-ThePlant #$Plant)

“Paris is the capital of France".
(#$capitalCity #$France #$Paris)
   COMMONSENSE REASONING
• Modelling commonsense inference required
  the development of entirely new paradigms
  for inference beyond classical logic
     • Non-monotonic reasoning
     • Probabilistic models
  AI RUNS INTO TROUBLE, AGAIN

• The CYC project started in 1984, and by common
  opinion is nowhere near finished
  – Hand-coding of commonsense FACTS is unfeasible
  – (We will get back to this point later when talking
    about socially constructed knowledge)
• Work on lower-level tasks such as speech
  perception revealed the impossibility of hand-
  coding commonsense RULES and assigning them
  priorities
SPEECH
         KEY AI DISCOVERIES, 3
• A theory of intelligence requires a theory of
  how commonsense knowledge and cognitive
  abilities are LEARNED
    THE MACHINE LEARNING YEARS
          (1985-PRESENT)
• The development of methods for learning
  from evidence started even before Dartmouth
• But machine learning has now taken center
  stage in AI
                CYBERNETICS
• McCulloch, Pitts (1943): first artificial neurons
  model (based on studies of real neurons)
KNOWLEDGE REPRESENTATION IN THE BRAIN
MODELS OF LEARNING BASED ON THE
    BRAIN: THE PERCEPTRON
LEARNING TO CLASSIFY OBJECTS
  ARTIFICIAL INTELLIGENCE TODAY
• Artificial intelligence as a science:
   – Artificial intelligence vs. Cognitive Science
• Artificial Intelligence as a technology
AI INDUSTRY: GOOGLE
AI INDUSTRY: ROBOTICS
      CONTENTS OF THE COURSE
• Knowledge Representation
   – A reminder about logic
   – Ontologies
   – Semantic networks
• Machine Learning
   – A reminder about statistics
   – Supervised learning
   – Unsupervised learning
• Putting it all together: Natural Language
   – A task that requires to use both knowledge
     representation and machine learning
       PRACTICAL INFORMATION
• 60 hours / 12 credits
• Timetable:
   – Mondays, 10-12 and 16-18 (lectures)
   – Tuesdays, 12-14 (labs / tutorials)
• Prerequisites
   – The ideal student would have the background provided by
     the three-year course in Filosofia and Informatica (some
     background in linguistic, logic, and statistics; some
     experience with programming)
• Evaluation
   – A project to be presented at the exam
• Web site: http://clic.cimec.unitn.it/massimo/Teach/AI
                      READING MATERIAL
• Required:
    – The course slides, available from the Web Site
    – Other material downloadable from the Website
• Recommended readings:
    – Russel and Norvig, Artificial Intelligence: A Modern Approach (2nd ed), Prentice-Hall
    – Bianchini, Gliozzo, Matteuzzi, Instrumentum vocale: intelligenza artificiale e linguaggio,
      Bononia
• Supplementary on specific sub-areas of AI:
    – KR:
         •   John F. Sowa, Knowledge Representation, Brooks / Cole
         •   Blackburn, Bos, Representation and Inference for Natural language, CSLI
    – ML:
         •   Mitchell – Machine Learning – Prentice-Hall
                                READINGS
• This lecture:
    – http://en.wikipedia.org/wiki/Timeline_of_artificial_intelligence
    – John F. Sowa, Knowledge Representation, Brooks / Cole, ch. 1

								
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