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Title: Artificial Intelligence CAP492


1
Artificial Intelligence CAP492
  • Dr. Souham Meshoul
  • Information Technology Department
  • CCIS King Saud University
  • Riyadh, Saudi Arabia
  • meshoul_at_ccis.ksu.edu.sa

2
INTRODUCTION TO ARTIFICIAL INTELLIGENCE


Chapter 1
3
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Goal of Artificial Intelligence Not only to
    understand how does mind work? but also how to
    build intelligent entities?.
  • Engineering point of view -Solve real-world
    problems using knowledge and reasoning
  • -Develop concepts,
    theory and practice of building intelligent
    entities
  • - Emphasis on system
    building
  • Scientific point of view - Use
    computers as a platform for studying intelligence
    itself

  • - Emphasis on understanding intelligent behavior.
  • Artificial Intelligence is one of the newest
    sciences which emerged after the world war II. AI
    represents a big and open field.
  • The name Artificial Intelligence was adopted for
    the first time in 1956. (Computational
    Intelligence)
  • Artificial Intelligence can be viewed as a
    universal field Ho to automate intellectual
    tasks?

4
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • What is artificial Intelligence?
  • Several definitions are available in the
    literature.
  • Thinking vs
    Behavior
  • Model humans vs Work
    from an ideal standard
  • Two points of views
  • 1. Thinking/Acting humanly success is
    measured in term of fidelity to human
    performance.
  • 2. Thinking/Acting rationally success
    is measured using an ideal concept of
    intelligence called
  • Rationality.
  • Rational System system which does the right
    thing given what it knows.

5
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Some AI Definitions
  • According to thought processes and reasoning
  • Thinking like humans
  • The exciting new effort to make computers
    thinkmachines with minds, in the full and
    literal sense. (Haugeland, 1985).
  • The automation of activities that we associate
    with human thinking, activities such as
    decision-making, problem solving, learning
    (bellman, 1978).
  • Thinking rationally
  • The study of mental faculties through the use
    of computational models. (Charniak and
    Mcdermott, 1985).
  • The study of the computations that make it
    possible to perceive, reason and act. (Winston
    1992).

6
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Some AI Definitions
  • According to behavior
  • Acting Humanly
  • The art of creating machines that perform
    functions that require intelligence when
    performed by people. (Kurzweil, 1990)
  • The study of how to make computers do things at
    which, at the moment, people are better. (Rich
    and Knight, 1991)
  • Acting rationally
  • Computational intelligence is the study of the
    design of intelligent agents. (Poole et
    al.,1998).
  • AIis concerned with intelligent behavior in
    artifacts. (Nilsson, 1998).

7
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Some other definitions
  • Weak AI (Searle) AI develops useful, powerful
    applications
  • Strong AI claims machines have cognitive minds
    comparable to humans
  • debates on strong AI bring up some difficult
    conceptual problems in philosophy..
  • Keep this one Science of making machines do
    things that would require intelligence if done by
    humans Marvin Minsky

8
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Acting Humanly
  • Turing Test Alan Turing 1950 Computing
    Machinery and Intelligence

9
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Test The computer passes the test of
    intelligence if a human, after posing some
    written questions, cannot tell whether the
    responses were from a person or not.
  • Provides an operational definition of AI.
  • Still being used today within the AI community in
    annual competitions the Loebner Prize (100,000
    and a true 18K gold medal)
  • To give an answer, the computer would need to
    posses some capabilities
  • Natural language processing To communicate
    successfully.
  • Knowledge representation To store what it knows
    or hears.
  • Automated reasoning to answer questions and draw
    conclusions using stored information.
  • Machine learning To adapt to new circumstances
    and to detect and extrapolate patterns.
  • Computer vision To perceive objects.
  • Robotics to manipulate objects and move.

10
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Example ELIZA program, some extracts from the
    test
  • Patient Men are all alike.
  • ELIZA In what way?
  • Patient They are always bugging about something
    or the other.
  • ELIZA Can you think of a specific example?
  • Patient Well, my boyfriend made me come here.
  • ELIZA Your boyfriend made you come here?
  • Patient He says Im depressed much of the time.
  • ELIZA I am sorry you are depressed.
  • Problems
  • ELIZA and similar programs stressed simple
    syntactic analysis and generation of sentences.
    They used pattern matching with known sentences.
  • Turings test is not reproducible or amenable to
    mathematical analysis (it suggests duplicate an
    examplar).
  • An alternative Study the underlying principles
    of intelligence (Wright brothers)

11
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Thinking Humanly
  • Program think like human ?
    How humans think?
  • Requires Scientific theories of internal
    activities of the brain (cognitive science and
    cognitive neuroscience).
  • Example
  • The General Problem Solver (GPS designed by
    Newell and Simon In 1963) was meant to be a
    program that simulated human thought.
  • GPS used means-end analysis in its search for
    solutions, computing the difference between the
    goal and current, and then attempting to minimize
    the difference.
  • Newell and Simon by comparing GPS traces with
    those of human subjects discovered that the
    behavior of GPS was largely a subset of human
    behavior

12
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Thinking rationally
  • The Laws of Thought approach is based on pattern
    for argument structure arising from Aristostles
    syllogisms.
  • Example, Socrates is a man all men are mortal,
    therefore Socrates is mortal. The laws of
    thought initiated the field of logic.
  • The formal logic movement was advanced by Peano,
    Boole, Frege,, Godell and others (late 1800s
    and early 1900s)
  • Inspired perhaps by early progress, Hibert became
    a proponent of a school of thought known as
    logicism or formalism. The goal of this was to
    devise a logic, or formal system, capable of
    deriving all mathematical theorems.

13
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Acting rationally
  • Modern AI can be characterized as the engineering
    of rational agents.
  • An agent is simply an entity that perceives and
    acts. A rational agent is an entity that
    perceives, reasons and acts rationally
    (correctly).

14
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Foundations
  • An interdisciplinary subject found on
  • Philosophy,
  • mathematics,
  • economics,
  • neuroscience,
  • psychology,
  • computer engineering,
  • linguistics, and so on

15
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • History of Artificial Intelligence
  • Big dream
  • Ultimately, we are dealing with the question
    What are we (human beings) doing when we are
    thinking?
  • Thought processes in the human mind are
    computational in nature. There are mechanistic
    procedures for generating these thoughts.
  • Such computations can be simulated and
    implemented by a Turing machine. Therefore, it
    can be programmed.

16
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • History of Artificial Intelligence
  • Early days (1943-1955)
  • 1943 first piece of AI work Warren McCulloch
    and Walter Pitts
  • Model of artificial neurons
  • Mathematical learnable functions that generate
    on/off depending on inputs (logic gates)
  • Any computable function can be computed by a
    network of connected neurons.
  • Suitably defined networks can learn.
  • 1949 Hebbian learning
  • A mechanism for updating the connection strength
    of a neuron.
  • Today, neurologists have confirmed that something
    similar to Hebbian learning indeed is going on in
    our brain when we are learning.
  • 1950 Turing test, complete vision of AI in
    computing machinery and Intelligence
  • 1951 first neural network computer
  • Implemented by M. Minsky and D. Edmonds

17
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • History of Artificial Intelligence
  • Early days (1943-1955) Mcculloch and pitts
    artificial neuron

0.3
1
-1
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1
0
0.5
18
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • History of Artificial Intelligence
  • Birth of AI 1956
  • 1956 Dartmouth Conference
  • Organized by John McCarthy and colleagues for
    starting a new area in studying computation and
    intelligence.
  • John McCarthy introduced the term artificial
    intelligence in the conference.
  • The next 20 years witnessed steady growth of the
    field led by the pioneers appeared in the
    Dartmouth conference.

19
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • History of Artificial Intelligence
  • Expectations and Initial enthusiasm (1952 1969)
  • 1956 Samuels checkers program
  • First game playing program achieving
    human-competitive performance.
  • 1957 Simons general problem solver (GPS)
  • Imitates the way a human would solve planning
    problems.
  • 1958 Invention of LISP by J. McCarthy.
  • The first AI programming language.
  • 1958 Minskys microworlds
  • The concept of creating a controlled
    environment in which problem solving appears to
    require intelligence was born. The study of
    computation and intelligence can become more
    manageable in these micro-worlds

20
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Expectations and Initial enthusiasm (1952 1969)
  • 1963 Thomas Evans program ANALOG
  • Solved analogy problems in an IQ test.
  • 1965 ELIZA
  • Simulates a dialog with a computer in English on
    any topic.
  • Became popular when programmed to simulate a
    psychotherapist (Fedoras Emacs).
  • 1967 Dendral program (developed at Stanford)
  • First successful program for scientific reasoning
    one of the earlier rule based expert systems. A
    program that can infer molecular structures given
    the information provided by a mass spectrometer
    (that gives the masses of the various fragments
    of a molecule). The program relies on expert
    knowledge (encoded as rules) to constraint the
    generation of possible molecular structures that
    are consistent with the information from the mass
    spectrometer

21
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • History of Artificial Intelligence
  • Reality Check (1966 1973) series of
    disappointments and frustrations
  • AI was poured
    little buckets of reality cold water
  • Problems
  • Most early systems contain little or no knowledge
    of their subject matter
  • Knowledge acquisition bottleneck.
  • Example Poor performance of earlier machine
    translation system (Russian ? English) the
    spirit is willing but the flesh is weak was
    translated to the vodka is good but the meat is
    rotten.
  • Computational Intractability of AI problems
  • Theory of computational complexity was not
    developed. Polynomial solvable problems,
    NP-completeness, etc
  • People thought a faster machine could solve any
    hard problem.
  • Initial frustration with theorem proving led to a
    disappointment in AI. Theorem proving is
    exponential in complexity

22
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • History of Artificial Intelligence
  • Resurgence (1969 1979)
  • 1971 T. Winograds Ph.D. thesis (MIT)
    demonstrated a system that can understand English
    in a micro-domain (the block world).
  • 1972 PROLOG was developed by a group of
    Europeans and became alternative to LISP as an AI
    programming language.
  • 1974 MYCIN was developed by Ted Shortliffe.
    Expert system for medical diagnosis. Sometimes
    called the first expert system.
  • 1978 The Version Space algorithm was developed
    by Tom Mitchell at Stanford. First symbolic
    machine learning algorithm. Father of Machine
    Learning.
  • 1979 Non-monotonic logic. Began to be formalized
    by John McCarthy and his colleagues.

23
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • History of Artificial Intelligence
  • Resurgence (1969 1979) Winograd 1972

24
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • History of Artificial Intelligence
  • AI becomes an industry (1980 present)
  • AI started to become industrially and
    commercially beneficial
  • 1982 R1 was deployed at DEC an expert system
    that saved the company around 40M / year
  • Du Pont had 100 in use and an estimated 500 in
    development at late 90s to early 21st century
  • At an international level, AI was considered a
    part of a countrys technological developments
  • Japan First Generation project (10 year plan
    to build intelligence machines running in Prolog)
  • USA Microelectronics and Computer Technology
    Corporation (MCC) was formed in response
  • Britain Funding for AI was reinstated

25
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • History of Artificial Intelligence
  • Renewing with connectionism and AI becomes a
    science (1986 present)
  • Work of the physicist John Hopfield (1982) on
    using techniques from statistical mechanics.
  • Connectionist models of intelligent systems
    competitor to the symbolic models (Newell and
    Simon) and logicist approach (McCarthy).
    (complementary approaches in fact).
  • Several revolutions in many fields pattern
    recognition, computer vision, robotics
  • Emergence of intelligent agents.

26
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Examples of AI applications Game Playing
  • TDGammon, the world champion backgammon player,
    built by Gerry Tesauro of IBM research.
  • Perception keyboard input.
  • Reason reinforcement learning.
  • Actuation graphical output shows dice and
    movement of piece.
  • Deep Blue chess program beat world champion Gary
    Kasparov
  • Perception input symptoms and test results.
  • Reason Bayesian networks, Monte-Carlo
    simulations.
  • Actuation output diagnoses and further test
    suggestions.

27
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Examples of AI applications Natural Language
    Understanding
  • Natural language understanding (spell checkers,
    grammar checkers)
  • AI translators spoken to and prints what one
    wants in foreign languages Alta Vistas
    translation of web pages.
  • Advanced systems can answer questions based on
    the information in the text and produce useful
    summaries.
  • PROVERB (Littman 1999) crossword puzzles
  • Examples of successes English conversation
  • START system accesses raw data tables, and then
    can carry on a dialogue

28
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Examples of AI applications Expert systems
  • In geology
  • prospector expert system carries evaluation of
    mineral potential of geological site or region
  • Diagnostic Systems
  • Pathfinder, a medical diagnosis system (suggests
    tests and makes diagnosis) developed by Heckerman
    and other Microsoft research
  • Microsoft Office Assistant in Office provides
    customized help by decision-theoretic reasoning
    by an individual user.
  • MYCIN system for diagnosing bacterial infections
    of the blood and suggesting treatments
  • System Configuration
  • "XCON" (for custom hardware configuration)
    configures computers doing work of 300 people
    using 10,000 rules

29
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Examples of AI applications Robotics
  • Robotics becoming increasing important in various
    areas like games, to handle hazardous conditions
    and to do tedious jobs among other things.
  • Examples automated cars, ping pong player,
    mining, construction, robot assistant in
    microsurgery,

30
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Main issues in AI
  • Representation
  • Search many tasks can be viewed as searching a
    very large problem space for solution space
  • Inference related to search, inferring other
    facts from some given facts. e.g., knowing all
    elephants have trunks and Jo is an elephant,
    can we answer does Jo have a trunk?
  • Learning inductive inference, neural networks,
    artificial life, genetic algorithms, evolutionary
    strategies
  • Planning starting with general facts about the
    world, facts about the effects of basic actions,
    facts about a particular situation, and a
    statement of a goal, generate a strategy for
    achieving that goal in terms of a sequence of
    primitive steps or actions

31
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
  • Summary
  • Intelligence is studied from many perspectives
    Are you concerned with thinking or behavior?
  • AI can help us solve difficult, real-world
    problems, creating new opportunities in business,
    engineering, and many other application areas.
  • The history of AI has had cycles of success,
    misplaced optimism, and resulting cutbacks in
    enthusiasm and funding. There have also been
    cycles of introducing new creative approaches and
    systematically refining the best ones.
  • AI has advanced more rapidly in the past decade
    because of greater use of the scientific method
    in experimenting with and comparing approaches.
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