Introduction (Chapter 1)

1
Introduction (Chapter 1)

• CPSC 386 Artificial Intelligence
• Ellen Walker
• Hiram College

2
Goals of this Course

• Become familiar with AI techniques, including
  implementation
• Be able to read and write AI programs in LISP,
  and to a lesser extent, Prolog and CLIPS
• Understand the theory behind the techniques,
  knowing which techniques to apply when (and why)
• Become familiar with a range of applications of
  AI, including classic and current systems.

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What is AI?

• Not just studying intelligent systems, but
  building them
• Psychological approach an intelligent system is
  a model of human intelligence
• Engineering approach an intelligent system
  solves a sufficiently difficult problem in a
  generalizable way

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Four Categories Of AI Definitions
Thinking Humanly The exciting new effort to make computers think machines with minds (Haugeland, 1985) Thinking Rationally The study of mental facilities through the use of computational models (Charniak McDermott, 1985)
Acting Humanly creating machines that perform functions that require intelligence when performed by people (Kurzweil, 1990) (Turing test) Acting Rationally AI is concerned with intelligent behavior in artifacts (Nilsson, 1998)
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Turing Test

• Given a communication terminal, can an observer
  determine whether the entity at the other end is
  human or machine?
• Tests acting like a human
• Does not test thinking like a human
• Does not test rational acting or thinking

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Foundations of AI (Sec. 1.2)

• Philosophy
• Rationality
• Mind vs. brain
• Knowledge and goals
• Mathematics
• Algorithms for reasoning (with uncertainty)
• Computability theory
• Economics
• Decision theory
• Game theory

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More Foundations

• Neuroscience
• Studying brains
• Psychology
• Studying behavior
• Cognitive modeling
• Computer science and engineering
• An artifact to make intelligent
• Control Theory Cybernetics
• Linguistics

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Eras of AI (sec. 1.3)

• Gestation (1943-1955)
• Early learning theory, first neural network,
  Turing test
• Birth (1956)
• Name coined by McCarthy
• Workshop at Dartmouth
• Early enthusiasm, great expectations (1952-1969)
• GPS, physical symbol system hypothesis
• Geometry Theorem Prover (Gelertner), Checkers
  (Samuels)
• Lisp (McCarthy), Theorem Proving (McCarthy),
  Microworlds (Minsky et. al.)
• neat (McCarthy _at_ Stanford) vs. scruffy
  (Minsky _at_ MIT)

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More Eras of AI

• Dose of Reality (1966-1973)
• Combinatorial explosion
• Knowledge-based systems (1969-1979)
• Weak methods vs. domain-specific knowledge
• AI Becomes an Industry (1980-present)
• Boom period 1980-88, then AI Winter
• Return of Neural Networks (1986-present)
• AI Adopts the Scientific Method (1987-present)
• Intelligent Agents (1995-present)
• SOAR, Internet as a domain
• Very Large Data Sets (2001-Present)

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What Makes a Solution AI?

• Not just the problem, also the generality of the
  solution
• Examples
• Tic Tac Toe
• Question Answering
• Speech understanding

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Tic Tac Toe 1

• Precompiled move table.
• For each input board, a specific move (output
  board)
• Perfect play, but is it AI?

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Tic Tac Toe 2

• Represent board as a magic square, one integer
  per square (834, 159, 672)
• If 3 of my pieces sum to 15, I win
• Predefined strategy
• 1. Win
• 2. Block
• 3. Take center
• 4. Take corner
• 5. Take any open square

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Tic Tac Toe 3

• Given a board, consider all possible moves
  (future boards) and pick the best one
• Look ahead (opponents best move, your best
  move) until end of game
• Functions needed
• Next move generator
• Board evaluation function
• Change these 2 functions (only) to play a
  different game!

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Question Answering

• Answer based on pattern matching
• Works in restricted domain (e.g. local driving
  directions, directory assistance)
• Knowledge stored as canned answers
• Match question to knowledge, then generate answer
• Wider variety of questions can be accommodated

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Speech Understanding

• Directly match digits to 1 through 9 patterns
• Learn to recognize 1 through 9 patterns by
  training (feature-based)
• Recognize numbers in context, e.g. phone number
  area code must be valid, prefer numbers in
  address book,