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CS3243 FOUNDATIONS OF ARTIFICIAL INTELLIGENCE
  • AY2004/2005 Semester 2
  • Introduction: Chapter 1
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CS3243
  • Course home page: http://www.comp.nus.edu.sg/~cs3243
  • IVLE for homework submission and forum communication.
  • Textbook: S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall, 2003, Second Edition
  • Lecturer: Min-Yen Kan (S15 05-05)
  • Grading: Programming assignments (20%, 20%), Midterm test (20%), Final exam (40%)
  • Class participation can only revise your grade upwards
  • Lecture and tutorial attendance is mandatory
  • Midterm test (in class, 1 hr) and final exam (2 hrs) are both closed book
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Outline
  • Course overview
  • What is AI?
  • A brief history
  • The state of the art
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Course overview
  • Introduction and Agents (chapters 1, 2)
  • Search (chapters 3, 4, 5, 6)
  • Logic (chapters 7, 8, 9)
  • Uncertainty (chapters 13, 14)
  • Learning (chapters 18, 20)
  • Optional Lectures:
    • Natural Language Processing (chapters 22, 23)
    • Planning and Robotics (chapters 11, 12, 25)
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What is AI?
  • Views of AI fall into four categories:


  • Thinking humanly Thinking rationally
  • Acting humanly Acting rationally


  • The textbook advocates "acting rationally"
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Acting humanly: Turing Test
  • Turing (1950) "Computing machinery and intelligence":
  • "Can machines think?" à "Can machines behave intelligently?"
  • Operational test for intelligent behavior: the Imitation Game





  • Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes
  • Anticipated all major arguments against AI in following 50 years
  • Suggested major components of AI: knowledge, reasoning, language understanding, learning
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Thinking humanly: cognitive modeling
  • 1960s "cognitive revolution": information-processing psychology
  • Requires scientific theories of internal activities of the brain
    • How to validate? Requires
      •     1) Predicting and testing behavior of human subjects (top-down)
      •     or 2) Direct identification from neurological data (bottom-up)
  • Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) are now distinct from AI
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Thinking rationally: "laws of thought"
  • Aristotle: what are correct arguments/thought processes?
  • Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts; may or may not have proceeded to the idea of mechanization
  • Direct line through mathematics and philosophy to modern AI
  • Problems:
    • Not all intelligent behavior is mediated by logical deliberation
    • What is the purpose of thinking? What thoughts should I have?
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Acting rationally: rational agent
  •  Rational behavior: doing the right thing
  • The right thing: that which is expected to maximize goal achievement, given the available information
  • Doesn't necessarily involve thinking – e.g., blinking reflex – but  thinking should be in the service of rational action
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Rational agents
  • An agent is an entity that perceives and acts
  • This course is about designing rational agents
  • Abstractly, an agent is a function from percept histories to actions:
  • [f: P* à A]
  • For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance
  • Caveat: computational limitations make perfect rationality unachievable
    • à design best program for given machine resources
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AI prehistory
  • Philosophy Logic, methods of reasoning, mind as physical
    system foundations of learning, language,
    rationality
  • Mathematics Formal representation and proof algorithms,
    computation, (un)decidability, (in)tractability,
    probability
  • Economics utility, decision theory
  • Neuroscience physical substrate for mental activity
  • Psychology phenomena of perception and motor control,
    experimental techniques
  • Computer building fast computers
    engineering
  • Control theory design systems that maximize an objective
    function over time
  • Linguistics knowledge representation, grammar
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Abridged history of AI
  • 1943     McCulloch & Pitts: Boolean circuit model of brain
  • 1950     Turing's "Computing Machinery and Intelligence"
  • 1956 Dartmouth meeting: "Artificial Intelligence" adopted
  • 1952–69 Look, Ma, no hands!
  • 1950s Early AI programs, including Samuel's checkers
    program, Newell & Simon's Logic Theorist,
    Gelernter's Geometry Engine
  • 1965 Robinson's complete algorithm for logical reasoning
  • 1966–73 AI discovers computational complexity
    Neural network research almost disappears
  • 1969–79 Early development of knowledge-based systems
  • 1980–  AI becomes an industry
  • 1986– Neural networks return to popularity
  • 1987– AI becomes a science
  • 1995– The emergence of intelligent agents
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State of the art
  • Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997
  • Proved a mathematical conjecture (Robbins conjecture) unsolved for decades
  • No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego)
  • During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people
  • NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft
  • Proverb solves crossword puzzles better than most humans