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344-571 ????????????? (Artificial Intelligence)

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Title: 344-571 ????????????? (Artificial Intelligence)


1
344-571 ????????????? (Artificial Intelligence)
  • ??.??.?????? ??????????????
  • ?????????????????????????? ??????????????
    ????????????????????????
  • ????????? CS 108 ???????? 074-288580
  • E-mail wwettayaprasit_at_yahoo.com
  • Website http//www.cs.psu.ac.th/wiphada

2
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  • 1. ???????????????????????????????????????????????
    ?????????????????????????????????
  • 2. ???????????????????????????????????????????????
    ?
  • 3. ?????????????????????????????????????????????
  • ?????????????????? ????????? ?????????
    ?????????????????????????
  • ???????? ?????????? 30 ??????????
    40
  • LAB Assignment 30
  • ???? Artificial Intelligence second edition,
    Elaine Rich and Kevin Knight,
  • McGraw-Hill Inc., 1991.

3
???????????
Chapter 1 What is Artificial Intelligence? Chapt
er 2 Problems and Spaces Chapter 3 Heuristic
Search Chapter 4 Natural Language Processing
Chapter 5 Machine Learning Chapter 6
Robotics Chapter 7 Neural Networks
Chapter 8 Expert Systems
Chapter 9 Computer Vision
4
Chapter 1What is Artificial Intelligence?

5
Content
  • Artificial IntelligenceArtificial Intelligence
    FieldsHeuristicTic Tac ToeTuring Test

6
Artificial Intelligence
  • artificial intelligence
  • n. (Abbr. AI)
  • The ability of a computer or other machine to
    perform those activities that are normally
    thought to require intelligence.
  • The branch of computer science concerned with the
    development of machines having this ability.

7
Artificial Intelligence
  • The subfield of computer science concerned with
    understanding the nature of intelligence and
    constructing computer systems capable of
    intelligent action.
  • It embodies the dual motives of furthering basic
    scientific understanding and making computers
    more sophisticated in the service of humanity.

8
Artificial Intelligence
  • Many activities involve intelligent action
  • problem solving, perception, learning,
    planning and other symbolic reasoning,
    creativity, language, and so forthand therein
    lie an immense diversity of phenomena.

9
Artificial Intelligence
  • Computer Encyclopedia
  • (Artificial Intelligence) Devices and
    applications that exhibit human intelligence and
    behavior including robots, expert systems, voice
    recognition, natural and foreign language
    processing. It also
    implies the ability to learn and adapt through
    experience.

10
Artificial Intelligence
  • Wikipedia
  • The term Artificial Intelligence (AI) was first
    used by John McCarthy who considers it to mean
    "the science and engineering of making
    intelligent machines".1
  • It can also refer to intelligence as exhibited
    by an artificial (man-made, non-natural,
    manufactured) entity.

11
Artificial Intelligence
  • Wikipedia
  • AI is studied in overlapping fields of computer
    science, psychology, neuroscience and
    engineering, dealing with intelligent behavior,
    learning and adaptation and usually developed
    using customized machines or computers.

12
History of Artificial Intelligence
1950 Alan Turing introduces the Turing test intended to test a machine's capability to participate in human-like conversation.
1951 The first working AI programs were written to run on the Ferranti Mark I machine of the University of Manchester a checkers-playing program written by Christopher Strachey and a chess-playing program written by Dietrich Prinz.
1956 John McCarthy coined the term "artificial intelligence" as the topic of the Dartmouth Conference.
1958 John McCarthy invented the Lisp programming language.
1965 Joseph Weizenbaum built ELIZA, an interactive program that carries on a dialogue in English language on any topic.
1965 Edward Feigenbaum initiated DENDRAL, a 10-yr effort to develop software to deduce the molecular structure of organic compounds using scientific instrument data. It was the first expert system.
13
History of Artificial Intelligence
1966 Machine Intelligence workshop at Edinburgh - the first of an influential annual series organized by Donald Michie and others.
1968 HAL 9000 made its appearance in the science fiction movie 2001 A Space Odyssey.
1972 The Prolog programming language was developed by Alain Colmerauer.
1973 Edinburgh Freddy Assembly Robot a versatile computer-controlled assembly system.
1974 Ted Shortliffe's PhD dissertation on the MYCIN program (Stanford) demonstrated a very practical rule-based approach to medical diagnoses, even in the presence of uncertainty. While it borrowed from DENDRAL, its own contributions strongly influenced the future of expert system development, especially commercial systems.
1997 The Deep Blue chess program (IBM) beats the world chess champion, Garry Kasparov.
1999 Sony introduces the AIBO, an artificially intelligent pet.
2004 DARPA introduces the DARPA Grand Challenge requiring competitors to produce autonomous vehicles for prize money.
14
Artificial Intelligence
Typical problems to which AI methods are applied Typical problems to which AI methods are applied
Pattern recognition Computer vision, Virtual reality and Image processing
Optical character recognition Diagnosis (artificial intelligence)
Handwriting recognition Game theory and Strategic planning
Speech recognition Game artificial intelligence and Computer game bot
Face recognition Natural language processing, Translation and Chatterbots
Artificial Creativity Non-linear control and Robotics
15
AI Areas
  • Artificial Intelligence (AI)
  • the branch o f computer science that is concerned
    with the automation of intelligent behavior.
  • AI Areas
  • Game Playing
  • Automated Reasoning and Theorem Proving
  • Expert Systems
  • Natural Language Understanding and Semantics
    Modeling
  • Modeling Human Performance
  • Planning and Robotics
  • Machine Leaning
  • Neural Networks

16
Task Domain of AI
  • Mundane Tasks mundane(??????) adj. ??????
  • Perception Vision, Speech
  • Natural language Understanding, Generation,
    Translation
  • Commonsense reasoning
  • Robot control
  • Formal Tasks
  • Games Chess
  • Mathematics Logic, Geometry
  • Expert Tasks
  • Engineering Design, Fault finding,
    Manufacturing planning
  • Scientific analysis
  • Medical diagnosis
  • Financial analysis

17
Artificial Intelligence Fields

18
Robotics
  • Shakey the Robot Developed in 1969 by the
    Stanford Research Institute, Shakey was the first
    fully mobile robot with artificial intelligence.
    Seven feet tall, Shakey was named after its
    rather unstable movements. (Image courtesy of The
    Computer History Museum, www.computerhistory.org)

19
Robotics
  • A legged game from RoboCup 2004 in Lisbon,
    Portugal
  • Team ENSCO's entry in the first Grand Challenge,
    DAVID

20
Robotics
  • The DARPA Grand Challenge is a race for a 2
    million prize where cars drive themselves across
    several hundred miles of challenging desert
    terrain without any communication with humans,
    using GPS, computers and a sophisticated array of
    sensors. In 2005 the winning vehicles completed
    all 132 miles of the course in just under 7 hours.

21
Robotics
  • robot A mechanical device that sometimes
    resembles a human and is capable of performing a
    variety of often complex human tasks on command
    or by being programmed in advance.
  • A machine or device that operates automatically
    or by remote control.
  • A person who works mechanically without original
    thought, especially one who responds
    automatically to the commands of others.

22
Robotics
  • Computer Encyclopedia
  • robot
  • A stand-alone hybrid computer system that
    performs physical and computational activities.
    Capable of performing many different tasks, it is
    a multiple-motion device with one or more arms
    and joints.
  • Robots can be similar in form to a human, but
    industrial robots do not resemble people at all.

23
Robotics
  • Huey, Dewey and Louie
  • Named after Donald Duck's famous nephews, robots
    at this Wayne, Michigan plant apply sealant to
    prevent possible water leakage into the car. Huey
    (top) seals the drip rails while Dewey (right)
    seals the interior weld seams. Louie is outside
    of the view of this picture. (Image courtesy of
    Ford Motor Company.)

24
Robotics
  • Inspect Pipes from the Inside
  • Developed by SRI for Osaka Gas in Japan, this
    Magnetically Attached General Purpose Inspection
    Engine (MAGPIE) goes inside gas pipes and looks
    for leaks. This unit served as the prototype for
    multicar models that perform temporary repairs
    while capturing pictures. (Image courtesy of SRI
    International.)

25
Robotics
  • Computers Making Computers
  • Robots, whose brains are nothing but chips, are
    making chips in this TI fabrication plant. (Image
    courtesy of Texas Instruments, Inc.)

26
Robotics
  • How Small Can They Get?
  • By 2020, scientists at Rutgers University believe
    that nano-sized robots will be injected into the
    bloodstream and administer a drug directly to an
    infected cell. This robot has a carbon nanotube
    body, a biomolecular motor that propels it and
    peptide limbs to orient itself.

27
Robotics
  • ASIMO,
  • a humanoid robot manufactured by Honda.

28
Three Laws of Robotics
  • A robot may not injure a human being or, through
    inaction, allow a human being to come to harm.
  • A robot must obey orders given it by human beings
    except where such orders would conflict with the
    First Law.
  • A robot must protect its own existence as long as
    such protection does not conflict with the First
    or Second Law.

29
Computer Vision
30
Computer Vision
  • Computer vision
  • The technology concerned with computational
    understanding and use of the information present
    in visual images.
  • In part, computer vision is analogous (similar)
    to the transformation of visual sensation into
    visual perception in biological vision.

31
Computer Vision
  • For this reason the motivation, objectives,
    formulation, and methodology of computer vision
    frequently intersect with knowledge about their
    counterparts in biological vision. However, the
    goal of computer vision is primarily to enable
    engineering systems to model and manipulate the
    environment by using visual sensing.

32
Computer Vision
  • Field of robotics in which programs attempt to
    identify objects represented in digitized images
    provided by video cameras, thus enabling robots
    to "see."
  • Much work has been done on stereo vision as an
    aid to object identification and location within
    a three-dimensional field of view. Recognition of
    objects in real time.

33
Computer Vision
  • Vision based biological species identification
    systems

34
Computer Vision
  • Artist's Concept of Rover on Mars,
  • an example of an unmanned land-based vehicle.
    Notice the stereo cameras mounted on top of the
    Rover. (credit Maas Digital LLC)

35
Neural Network
  • neural network also neural net n.
  • A real or virtual device, modeled after the human
    brain, in which several interconnected elements
    process information simultaneously, adapting and
    learning from past patterns

36
Neural Network
  • Computer Encyclopedia
  • neural network
  • A modeling technique based on the observed
    behavior of biological neurons and used to mimic
    (imitate) the performance of a system.

37
Neural Network
  • It consists of a set of elements that start out
    connected in a random pattern, and, based upon
    operational feedback, are molded into the pattern
    required to generate the required results.
  • It is used in applications such as robotics,
    diagnosing, forecasting, image processing and
    pattern recognition.

38
Neural Network
39
Machine Learning
40
Neural Network
  • Accounting Dictionary
  • Neural Networks
  • Technology in which computers actually try to
    learn from the data base and operator what the
    right answer is to a question.

41
Neural Network
  • The system gets positive or negative response to
    output from the operator and stores that data so
    that it will make a better decision the next
    time.
  • While still in its infancy, this technology shows
    promise for use in accounting, fraud detection,
    economic forecasting, and risk appraisals.
  • The idea behind this software is to convert the
    order-taking computer into a "thinking" problem
    solver.

42
Neural Network
  • Britannica Concise Encyclopedia
  • neural network
  • Type of parallel computation in which computing
    elements are modeled on the network of neurons
    that constitute animal nervous systems.
  • This model, intended to simulate the way the
    brain processes information, enables the computer
    to "learn" to a certain degree.

43
Neural Network
  • A neural network typically consists of a number
    of interconnected processors, or nodes. Each
    handles a designated sphere of knowledge, and has
    several inputs and one output to the network.
    Based on the inputs it gets, a node can "learn"
    about the relationships between sets of data,
    sometimes using the principles of fuzzy logic.

44
Neural Network
  • Neural networks have been used in pattern
    recognition, speech analysis, oil exploration,
    weather prediction, and the modeling of thinking
    and consciousness.

45
Machine Learning
  • Sci-Tech Dictionary
  • machine learning (m?'shen 'l?rni?)
  • (computer science) The process or technique by
    which a device modifies its own behavior as the
    result of its past experience and performance.

46
Machine Learning
  • Wikipedia
  • machine learning is concerned with the
    development of algorithms and techniques that
    allow computers to "learn".
  • At a general level, there are two types of
    learning inductive, and deductive. Inductive
    machine learning methods extract rules and
    patterns out of massive data sets.

47
Machine Learning
  • inductive,
  • Logic.
  • The process of deriving general principles from
    particular facts or instances.
  • Mathematics.
  • A two-part method of proving a theorem involving
    an integral parameter. First the theorem is
    verified for the smallest admissible value of the
    integer. Then it is proven that if the theorem is
    true for any value of the integer, it is true for
    the next greater value. The final proof contains
    the two parts.


48
Machine Learning
  • inductive,
  • reasoning from detailed facts to general
    principles
  • Rule induction is an area of machine learning in
    which formal rules are extracted from a set of
    observations.


49
Machine Learning
  • deductive. Logic.
  • The process of reasoning in which a conclusion
    follows necessarily from the stated premises
    inference by reasoning from the general to the
    specific.
  • reasoning from the general to the particular
  • Deduction is the process of drawing conclusions
    from premises


50
Machine Learning
  • Deduction The process of reaching a conclusion
    through reasoning from general premises to a
    specific premise.
  • An example of deduction is present in the
    following syllogism
  • Premise All mammals are animals.
  • Premise All whales are mammals.
  • Conclusion Therefore, all whales are animals.


51
Machine Learning
  • deduction, in logic, form of inference such that
    the conclusion must be true if the premises are
    true.
  • For example,
  • if we know that.. all men have two legs
  • And that ..John is a man,
  • it is then logical to deduce that ..John
    has two legs.


52
Expert System
  • expert systemn. Computer Science.
  • A program that uses available information,
    heuristics, and inference to suggest solutions to
    problems in a particular discipline.

53
Expert System
  • Expert systems
  • Methods and techniques for constructing
    human-machine systems with specialized
    problem-solving expertise.
  • The pursuit of this area of artificial
    intelligence research has emphasized the
    knowledge that underlies human expertise and has
    simultaneously decreased the apparent
    significance of domain-independent
    problem-solving theory. In fact, new principles,
    tools, and techniques have emerged that form the
    basis of knowledge engineering.

54
Expert System
  • Expertise consists of knowledge about a
    particular domain, understanding of domain
    problems, and skill at solving some of these
    problems.
  • Knowledge in any specialty is of two types,
    public and private.
  • Public knowledge includes the published
    definitions, facts, and theories which are
    contained in textbooks and references in the
    domain of study. But expertise usually requires
    more than just public knowledge.

55
Expert System
  • Human experts generally possess private knowledge
    which has not found its way into the published
    literature.
  • This private knowledge consists largely of rules
    of thumb or heuristics.
  • Heuristics enable the human expert to make
    educated guesses when necessary, to recognize
    promising approaches to problems, and to deal
    effectively with erroneous or incomplete data.

56
Expert System
Category Problem addressed
Interpretations Inferring situation descriptions from sensor data
Prediction Inferring likely consequences of given situations
Diagnosis Inferring system malfunctions from observables
Design Configuring objects under constraints
Planning Designing actions
Monitoring Comparing observations to plan vulnerabilities
Debugging Prescribing remedies for malfunctions
Repair Executing a plan to administer a prescribed remedy
Instruction Diagnosing, debugging, and repairing students' knowledge
57
Natural Language Processing
  • Wikipedia
  • Natural language processing (NLP) is a subfield
    of artificial intelligence and linguistics. It
    studies the problems of automated generation and
    understanding of natural human languages.
  • Natural language generation systems convert
    information from computer databases into
    normal-sounding human language, and natural
    language understanding systems convert samples of
    human language into more formal representations
    that are easier for computer programs to
    manipulate.

58
Natural Language Processing
  • We gave the monkeys the bananas because they were
    hungry and We gave the monkeys the bananas
    because they were over-ripe.
  • have the same surface grammatical structure.
    However, in one of them the word they refers to
    the monkeys, in the other it refers to the
    bananas
  • the sentence cannot be understood properly
    without knowledge of the properties and behaviour
    of monkeys

59
Natural Language Processing
Time flies like an arrow
  • A string of words may be interpreted in myriad
    ways. For example,
  • time moves quickly just like an arrow does
  • measure the speed of flying insects like you
    would measure that of an arrow - i.e. (You
    should) time flies like you would an arrow.
  • measure the speed of flying insects like an arrow
    would - i.e. Time flies in the same way that an
    arrow would (time them).
  • measure the speed of flying insects that are like
    arrows - i.e. Time those flies that are like
    arrows
  • a type of flying insect, "time-flies," enjoy
    arrows (compare Fruit flies like a banana.)

60
Natural Language Processing
  • "pretty little girls' school"
  • English and several other languages don't specify
    which word an adjective applies to.
  • For example, in the string "pretty little girls'
    school".
  • Does the school look little?
  • Do the girls look little?
  • Do the girls look pretty?
  • Does the school look pretty?

61
Question Answering 1
  • Russia massed troops on the Czech border.
  • POLITICS program Corbonell,1980)
  • Q1 Why did Russia do this?
  • A1...............................................
    .......................
  • Q1 What should the United States do?
  • A2 ..............................................
    ....................... OR
  • A2................................................
    .........................

62
Question Answering 2
  • Mary went shopping for a new coat.
  • She found a red one she really liked.
  • When she got it home, she discovered that it went
    perfectly with her favorite dress.

ELIZA Q1What did Mary go shopping for? A1
............................................. Q2W
hat did Mary find she liked? A2..................
........................... Q3 Did Mary buy
anything ? A3....................................
.........
63
Intelligence require knowledge
  1. It is voluminous.
  2. It is hard to characterize accurately.
  3. It is constantly changing.
  4. It differs from data by being organized in a way
    that corresponds to the ways it will be used.

64
Knowledge Representation and Search for AI
  • The knowledge captures generalizations.
  • It can be understood by people who must provide
    it.
  • It can easily be modified to correct errors and
    to reflect changes in the world.
  • It can be used in many situations even if it is
    not totally accurate or complete.
  • It can use to narrow the range of possibilities
    that must usually be considered.

65
Common Features of AI Problems
  1. The use of computer to do the symbolic reasoning.
  2. A focus on problems that do not respond to
    algorithmic solutions. ? Heuristic search.
  3. Manipulate the significant quantitative features
    of a situation rather than relying on numeric
    methods.
  4. Dealing with semantic meaning.
  5. Answer that are neither exact nor optimal but
    sufficient.
  6. Domain specific knowledge in solving problems.
  7. Use meta-level knowledge.

66
Heuristic
  • heuristic (hy?-ris'tik) adj.
  • Of or relating to a usually speculative
    formulation serving as a guide in the
    investigation or solution of a problem

67
Heuristic
  • Of or constituting an educational method in which
    learning takes place through discoveries that
    result from investigations made by the student.
  • Computer Science. Relating to or using a
    problem-solving technique in which the most
    appropriate solution of several found by
    alternative methods is selected at successive
    stages of a program for use in the next step of
    the program.

68
Heuristic
  • Computer Encyclopedia
  • heuristic
  • A method of problem solving using exploration and
    trial and error methods. Heuristic program design
    provides a framework for solving the problem in
    contrast with a fixed set of rules (algorithmic)
    that cannot vary.

69
Heuristic
  • Business Dictionary
  • Heuristic
  • Method of solving problems that involves
    intelligent trial and error, such as playing
    chess. By contrast, an algorithmic solution
    method is a clearly specified procedure that is
    guaranteed to give the correct answer.

70
tic tac toe
71
Tic Tac Toe
72
3D Tic Tac Toe
73
Homework 1
Tic-Tac-Toe
  • Read program 1, 2 and 3 and discuss the following
    criteria.
  • Their Complexity
  • Their use of generalization.
  • The clarity of their knowledge.
  • The extensibility of their approach.

74
Tic-Tac-Toe Program 1
75
Tic-Tac-Toe Program 1
76
Tic-Tac-Toe Program 1
  • Board nine element vector representation.
  • 0 blank, 1 X, 2
    O
  • Moveable Their Complexity 39 19,683
  • view vector board as a ternary number (base
    three)

77
Tic-Tac-Toe Program 2
78
Tic-Tac-Toe Program 2
79
Tic-Tac-Toe Program 2
2 blank
3 X
5 O
  • Board nine element vector representation.
  • an integer indicating which move of the game is
    about to played.
  • 1 indicate the first move.
  • 9 indicate the last move.
  • Board5 2 ? mean blank
  • Poswin(p) If it produce (332) 18 ? X can
    win
  • p 0 if the player can not win on his next move.
  • Poswin(p) If it produce (552) 50 ?O can
    win
  • Go(n) Make a move on square n.
  • TURN is odd ? if it is playing X
  • TURN is even ? if it is playing O
  • More efficient in term of space.

80
Tic-Tac-Toe Program 2
81
Tic-Tac-Toe Program 2
82
Tic-Tac-Toe Program 2
  • Board nine element vector representation.
  • 2 blank, 3 X, 5
    O
  • an integer indicating which move of the game is
    about to played.
  • 1 indicate the first move.
  • 9 indicate the last move.
  • Board5 2 ? mean blank
  • Poswin(p) If it produce MAGIC SQUARE
  • (8 3 4) 15
  • p 0 if the player can not win on his next move.
  • Go(n) Make a move on square n.
  • TURN is odd ? if it is playing X
  • TURN is even ? if it is playing O
  • More efficient in term of space.

83
Tic-Tac-Toe Program 3
84
Tic-Tac-Toe Program 3
85
Tic-Tac-Toe Program 3
  • Board_Position nine element vector representing
    the board, a list of board positions that could
    result from the next move, and a number
    representing as estimate of how likely the board
    position is lead to an ultimate win for the
    player to move.
  • Minimax Procedure in chapter 12.
  • We maximize the likely hood of winning the game,
  • While opponent Minimize the likely hood of
    winning the game
  • Decide which of a set of board positions is best.
  • find highest possible rating.
  • consider all the moves the component could make
    next.
  • ? See which move is worst for us....
  • (Assume the opponent will make that move)
  • Look forward many steps in advance.
  • Search tree need more time
  • Use AI technique

86
The level of the model
  1. What is the goal in trying to produce programs
    that do intelligent things that people do?
  2. Are we trying to produce programs that do the
    tasks the same way people do?
  3. Are we attempting to produce programs that simply
    do the tasks in whatever way appears easiest?

87
Model human performance
  • To test psychological theories of human
    performance.
  • PAPPY Colby, 1975
  • To enable computers to understand human
    reasoning.
  • To enable computers to understand computer
    reasoning.

88
TURING TEST
Columbia Encyclopedia
Alan Mathison Turing
89
TURING TEST
  • Columbia Encyclopedia
  • Turing test, a procedure to test whether a
    computer is capable of humanlike thought. As
    proposed (1950) by the British mathematician Alan
    Turing, a person (the interrogator) sits with a
    teletype machine isolated from two
    correspondentsone is another person, one is a
    computer.

Columbia Encyclopedia
90
TURING TEST
  • By asking questions through the teletype and
    studying the responses, the interrogator tries to
    determine which correspondent is human and which
    is the computer.

Columbia Encyclopedia
91
TURING TEST
  • The computer is programmed to give deceptive
    answers, e.g., when asked to add two numbers
    together, the computer pauses slightly before
    giving the incorrect sum to imitate what a
    human might do, the computer gives
    an incorrect answer slowly since the interrogator
    would expect the machine to give the correct
    answer quickly.
  • If it proves impossible for the interrogator to
    discriminate between the human and the computer,
    the computer is credited with having passed the
    test.

Columbia Encyclopedia
92
Criteria for success
  • How will we know if we have succeeded?
  • Turing test. Human Computer Person
    asking?
  • DENDRAL is a program that analyzes organic
    compounds to determine their structure.
  • HUMAN CHEMIST
    COMPUTER

93
Homework 1
  • 1. Given the meaning of Artificial Intelligence
    from your point of view. You may add citation
    from searching documents in the web or from the
    text book.
  • 2. Given all AI fields with some explanations.

94
Answers.com
95
The End
The road to success is always
under construction
Jim Miller
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