Introduction to Artificial Intelligence and Expert Systems

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Introduction to Artificial Intelligence and
Expert Systems
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AIs Beginnings

• 1956 Dartmouth Summer Seminar
• Attendees are considered the fathers of AI (AI
  has no mothers).
• Believed that computers could be used to process
  symbols rather than simply numbers.
• Many presented research
• Logic Theorist (Newell Simon)

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What is Artificial Intelligence?
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Definition of AI

• A branch of computer science concerned with the
  design and implementation of intelligent computer
  systems. Where an intelligent computer system is
  one that exhibits the characteristics associated
  with intelligence in human behavior
  understanding language, learning, reasoning,
  problem solving, etc.

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Different Views of AI

• Weak view
• Use intelligent programs to test theories about
  how human beings carry out cognitive operations.
• AI is the study of mental faculties through the
  use of computational models.
• Computer-based system that acts in such a way
  (i.e., performs tasks) that if done by a human we
  would call it intelligent or requiring
  intelligence.

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• ARTIFICIAL INTELLIGENCE IS BETTER THAN NO
  INTELLIGENCE AT ALL

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• Strong view
• The effort to develop computer-based systems that
  behave as humans.
• Argues that an appropriately programmed computer
  really is a mind, that understands and has
  cognitive states.
• The study is to proceed on the basis of the
  conjecture that every aspect of learning or any
  other feature of intelligence can in principle be
  so precisely described that a machine can be made
  to simulate. (From Dartmouth conference.)

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HALs last words, 2001 A Space Odyssey

• Good afternoon, gentleman. I am HAL 9000
  computer. I became operational at the HAL plant
  in Urbana, Ill., on the 12th of January, 1992.
  My instructor was Mr. Langley and he taught me to
  sing a song. If youd like to hear it, I can
  sing it for you.

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Branches of AI

• Games - study of state space search, e.g., chess
• Automated reasoning and theorem proving, e.g.,
  logic theorist
• Expert/Knowledge-based systems
• Natural language understanding and semantic
  modeling
• Model human cognitive performance
• Robotics and planning
• Automatic programming
• Learning
• Vision

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Development of AI

• General Problem Solvers (1950s)
• Power (1960s)
• Romantic Period (mid 1960s to mid 1970s)
• Knowledge-based Approaches (mid 1970s to mid
  1990s)
• Biological and Social Models (mid 1990s to
  current)

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General Problem Solvers

• use a generalized problem solving method (divide
  up problems, work forward, work backward) and
  apply approach to a VERY BROAD range of problems.
• limitations
• hardware capabilities
• sometimes called "weak solution methods"

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Examples of General Problem Solvers

• LOGIC THEORIST
• could prove 38 of the first 52 theorems in the
  Principia Mathematica
• Journal of Symbolic Logic declined publishing an
  article with Logic Theorist as a co-author
• GENERAL PROBLEM SOLVER
• used means-ends analysis to reduce the difference
  between the current state and desired (end) state
• handled mathematical logic problems, hence not as
  general as originally hoped

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POWER

• Throw immense computing power at certain types of
  problems
• example CHESS
• examine 10120 moves to consider all
  possibilities.
• advances that made this approach possible
• more powerful hardware
• languages developed specifically for AI
  applications

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ROMANTIC PERIOD

• Focus on computer understanding
• Hence concentrated on
• natural language - particularly stories and
  dialog.
• Fundamental problem
• what is "understanding"?
• Can a computer be said to understand things?
• Not clear how humans understand things, for
  instance reading in young children.

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Examples of Romantic Period Systems

• SHRDLU - developed by Winograd
• understood and reasoned about its block world
• ELIZA - Rogerian therapist
• Newell and Simon examined human problem solving
  via protocol analysis to determine how humans
  understand things. Then created computer
  programs to generate a similar trace.

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Place the green block on the red block.
In the handout, what types of reasoning can you
recognize?
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ELIZA sample dialog

• HUMAN You are like my father in some ways.
• ELIZA What resemblance do you see?
• HUMAN You are not very aggressive, but I think
  you dont want me to notice that.
• ELIZA What makes you think I am not very
  aggressive?
• HUMAN You dont argue with me.
• ELIZA Why do you think I dont argue with you?
• HUMAN You are afraid of me.
• ELIZA Does it please you to believe I am afraid
  of you?
• HUMAN My father is afraid of everybody.
• ELIZA What else comes to mind when you think of
  your father?
• HUMAN Bullies.

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KNOWLEDGE BASED APPROACHES - KNOWLEDGE
BASE/EXPERT SYSTEMS

• Flaws of previous approaches considered
• General problem solving tries to apply a single
  solution approach to a wide range of problems.
  The general approaches were not as general as
  hoped and more problem specific approaches could
  be more powerful and simpler.

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KBS (continued)

• Power approach tried to program optimal (highest
  probability) approach. Human experts use
  HEURISTICS (rules of thumb) to find a solution.
• Example Chess masters don't look ahead very many
  moves, as a POWER approach implies. Instead they
  choose from a set of good alternatives.

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KBS (continued)

• Romantic period true understanding may not be
  necessary to achieve useful results.
• Feigenbaum, in a speech at Carnegie, challenged
  his former professors to stop looking at "toy
  problems" and apply AI techniques to "real
  problems".
• The key to solving real world problems is that
  these system handle only a very specific problem
  area, a "narrow domain".

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Biological and Social Models

• Neural Networks (connectionist models in the text
  book)
• Based on the brains ability to adapt to the
  world by modifying the relationships between
  neurons.
• Genetic algorithms attempt to replicate
  biological evolution.
• Populations of competing solutions are generated.
• Poor solutions die out, better ones survive and
  reproduce with mutations created.
• Software agents
• Semi-autonomous agents, with little knowledge of
  other agents solve part of a problem, which is
  reported to other agents.
• Through the efforts of many agents a problem is
  solved.

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What is Intelligence?
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What attributes would you expect an Intelligent
Agent to exhibit?
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Turing Test
AI system
Experimenter
Control
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Appeal of the Turing Test

• Provides an objective notion of intelligence,
  i.e., compare intelligence of the system to
  something that is considered intelligent,
  avoiding debates over what is intelligence.
• Avoids debates of whether or not the system uses
  correct internal processes.
• Eliminates biases toward living organisms since
  experimenter communicates with both the AI system
  and the control (human) in the same manner.

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Weaknesses of the Turing Test

• The breadth of the test is nearly impossible to
  achieve.
• Some systems exhibit characteristics similar to
  Turings criteria, yet we would not label them
  intelligent e.g., ELIZA is easy to unmask, it
  cannot pass a true interrogation.
• Focuses on symbolic, problem solving ignores
  perceptual skills and manual dexterity which are
  important components of human intelligence.
• By focusing on replicating human intelligence,
  researchers may be distracted from the tasks of
  developing theories that explain the mechanisms
  of human and machine intelligence and applying
  the theories to solving actual problems.

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The Chinese Room
She does not know Chinese
Correct Responses
Chinese Writing is given to the person
Set of rules, in English, for transforming phrases
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The Chinese Room Scenario

• An individual is locked in a room and given a
  batch of Chinese writing. The person locked in
  the room does not understand Chinese.
• Next she is given more Chinese writing and a set
  of rules (in English which she understands) on
  how to collate the first set of Chinese
  characters with the second set of Chinese
  characters.
• If the person becomes good at manipulating the
  Chinese symbols and the rules are good enough,
  then to someone outside the room it appears that
  the person understands Chinese.

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Does the person understand Chinese?

• Why?
• Why not?

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The Chinese Room (cont.)

• Searle's, who developed the argument, point is
  that she doesn't really understand Chinese, she
  really only follows a set of rules.
• Following this argument, a computer could never
  be truly intelligent, it is only manipulates
  symbols. The computer does not understand the
  semantic context.
• Searles criteria is intentionality, the entity
  must be intentionally exhibiting the behavior,
  not simply following a set of rules.
• Intentionality is as difficult to define as
  intelligence.
• Searle excludes weak AI from his argument
  against the possibility of AI.

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Searles argument created a huge response

• This religious diatribe against AI, masquerading
  as a serious scientific argument, is one of the
  wrongest, most infuriating articles I have ever
  read in my life. ... I know that this journal is
  not the place for philosophical and religious
  commentary, yet it seems to me that what Searle
  and I have is, at the deepest level, a religious
  disagreement and I doubt that anything I say
  could ever change his mind. He insists on things
  he calls "causal intentional properties" which
  seem to vanish as soon as you analyze them, find
  rules for them, or simulate them. But what those
  things are, other than epiphenomena, or
  innocently emergent qualities I don't know.

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What is artificial intelligence?

• Arguments about AI seem to rapidly break down
  into philosophical debates where there is
  probably no absolute right or wrong answer.
• Note Hofstadter's comments about "religious"
  disagreement. It often comes down to considering
  the pros and cons of both sides, realizing that
  neither is completely right (or completely wrong)
  and taking a stand for one or the other.
• Which side you tend to fall on will, almost
  unavoidably, be based on personal values.

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• ARTIFICIAL INTELLIGENCE IS BETTER THAN NO
  INTELLIGENCE AT ALL

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Summary

• No universally accepted definition of
  intelligence.
• Definitions of intelligence is subject to change,
  which makes it difficult to aim for! Similar to
  the situation in linguistics and for comparative
  psychologists that have taught primates sign
  language.
• "The Ultimate Limits of AI - notice that these
  are really sociological questions.
• This course will focus what has been achieved in
  AI and Expert System. However, be aware of these
  issues.