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Introduce to Artificial Intelligence

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Title: Introduce to Artificial Intelligence


1
Introduce to Artificial Intelligence
  • 07/17/2009

2
Theories, Tools, Tests and Tying It All Together
  • Expert Systems

3
Expert System
  • knowledge-based systems (kbs)
  • intelligent knowledge-based systems (ikbs)
  • Definitions
  • Barr and Fiegenbaum, 1981
  • Gaschnig, Reboh and Reiter 1981

4
Programming Methodology
  • Domain Knowledge
  • Problem-Solving Methods
  • Mary has a fever this implies that she has an
    infection
  • fever (Mary) ? infection (Mary)
  • if Mary has a fever
  • then Mary has an infection

5
(No Transcript)
6
Expert System Components
7
Expert System Tools
  • Algorithmic languages.
  • (such as 'C', Pascal, Basic)
  • Symbolic languages.
  • (such as Prolog, LISP)
  • Development Environments.
  • (such as Art, KEE, LOOPS)
  • Expert System Shells.
  • (such as Crystal, XpertRule, Leonardo, Xi-Plus)

8
Symbolic Languages
  • LISP
  • This language contains a set of primitive
    operators that enable it to carry out several
    kinds of deductions with lists containing
    arbitrary strings of characters representing
    predicates and their arguments
  • (Charniak and McDermott 1985)
  • Prolog
  • Prolog is a higher level language than LISP in
    that it has deductive and search capability
    already built in.
  • Prolog is a vehicle for declarative programming
    by providing a Prolog program with a set of
    statements or axioms describing some system, it
    deduces desired additional facts
  • (Clocksin and Mellish 1981)

9
Prolog
  • Prolog is based on Pedicate Calculus Logic
  • John Likes Flowers
  • Likes (John, Flowers)
  • ? Likes (John, X)
  • X Flowers

10
Prolog
  • Prolog is based on Predicate Calculus Logic
  • John likes a person if that person likes wine and
    likes food.
  • likes (X, wine) ? likes (X, food) ? likes (John,
    X)
  • ? likes (John, Mary) (Deductive Reasoning is now
    needed)

11
Knowledge Acquisition
12
Knowledge Acquisition
  • Key issues confronting the designer of an AI
    system are
  • knowledge acquisition
  • knowledge representation
  • knowledge manipulation

13
Knowledge Acquisition
  • Elicitation must carry out several operations,
    the most important of which are the following
  • Extracting the knowledge by externalising it.
  • Rendering it explicit by accumulating sufficient
    detail to make it clear.
  • Record it in a symbolic form.
  • Verify it by checking the symbolic form against
    the original statement.

14
Knowledge Acquisition Source of Knowledge
  • Expert Opinion
  • Historic Data
  • Codes of Practice
  • Standard Engineering Procedures
  • Experimental Data
  • Technical Literature
  • Text Books
  • Journals
  • Manuals
  • Manufacturers Information
  • Established Engineering Equations

15
Stages of Acquisition
  • Define task
  • Build-up Domain Vocabulary
  • Words, phrases, formulae that make up the
    natural language of the task.
  • Develop a Model of the Reasoning Involved and how
    it is applied.
  • Flowcharts and decision trees often used.
  • Protocol Analysis.
  • Paper exercise - no programming at this stage.
  • Iterative procedure with Experts

16
Spider Diagrams
  • Simple enough that the structure is self evident
    even to newcomers
  • Powerful enough to express complex structures
  • Flexible enough to accommodate the inevitable
    flow of changes and revisions

17
Tutorial 1 Spider Diagram
Type of Light
Best Plant
  • The Begonia likes bright light and survives
    best outdoors in natural sunlight, whereas Ivy
    prefers dim light and is probably better indoors
    lit by a light bulb.

Light
Location
18
Tutorial 1
  • Domain Dictionary
  • Light Bright or Dim
  • Type of Light Sunlight or Light Bulb
  • Location Indoor or Outdoor

19
Tutorial 1 Decision Tree
Location
Type of Light
Outdoor
Light Bulb
Indoor
Sunlight
Type of Light SUNLIGHT
Type of Light LIGHT BULB
Light DIM
Light BRIGHT
Light
Dim
Bright
Best Plant Ivy
Best Plant Begonia
20
Problems, Problem Spaces and Search
  • Weak Search Techniques
  • Strong Search Techniques

21
Defining a Problem as a Search Space
  • Many problems exhibit no detectable regular
    structure to be exploited, they appear chaotic,
    and do not yield to efficient algorithms.
  • Exhaustive search of large state spaces appears
    to be the only viable approach.
  • We survey techniques for exhaustive search and
    present some examples of intelligent, heuristic
    search.
  • The concept of search plays an ambivalent role in
    science and engineering, in one way, any problem
    whatsoever can be seen as a search for the right
    answer.

22
Formulation and Representation of Problems
  • To solve problems that are of interest to
    scientists and engineers we need to apply a
    common vocabulary.
  • Nodes
  • Search Trees
  • Decision Trees
  • Search Graphs
  • Search Space

23
(No Transcript)
24
Weak Search Strategies
  • We need to study the question of how to decide
    which strategy to apply and even what the
    strategies are.
  • Its called the weak methods because although
    they are very general they lack the power of
    knowledge-guided search.
  • Weak searches can usually be broken down into two
    forms of search,
  • depth-first search
  • breadth-first search

25
depth-first search
  • Depth-first search (DFS) is the prime candidate.
  • Its simple logic
  • keep going as long as you see anything new,
    and when that is not possible, back up as far as
    necessary and proceed in a new direction.

26
Strong Search
  • The general search methods discussed do not make
    use of domain knowledge and are considered as
    weak methods simply because they do not exploit
    such knowledge.
  • In order to solve many problems efficiently it is
    often necessary to construct a control structure
    that is no longer guaranteed to find the best
    answer, but will almost always find a very good
    answer. thus we introduce the idea of an
    heuristic.
  • Hill Climbing
  • Best first Search

27
Heuristic
  • Heuristic A technique which improves the
    efficiency of a search process, possibly by
    sacrificing claims of completeness.
  • Heuristics are key terms in many branches of AI.A
    heuristic is best defined as a 'rule of thumb' or
    piece of advice that is usually based on prior
    experience and not guaranteed to work.

28
Hill Climbing
  • Hill climbing is a variant of generate and test
    in which feedback from the test procedure is used
    to help the generator decide which direction to
    move in the search space.
  • The test function has a heuristic function (or
    objective function) that provides an estimate of
    how close a given state is to a goal state, the
    generate procedure can exploit this.

29
Best First Search
30
Search Problems?
  • Three foxes and three chickens seek to cross a
    river. A boat is available which can hold two
    animals and which can be navigated by any
    combination of foxes and chickens involving one
    or two animals.
  • The chickens insist on never being left in a
    minority on either riverbank, for fear of being
    eaten by a majority of foxes.
  • Find a schedule of crossings that will permit all
    the foxes and chickens to cross the river safely.

31
F
F
C
C
C
F
B
  • If all the generated nodes are expanded we
    generate multiple copies of many nodes.
  • Also many nodes which are generated are
    unacceptable.
  • (Note Generate and Test)

32
Solving
33
UNFIT
UNFIT
UNFIT
34
Knowledge Representation
35
Knowledge Representation
  • We call these representations of knowledge
    knowledge bases, and the manipulative operations
    on these knowledge bases, inference engine
    programs.

36
What to represent
  • Facts truths about the real world and what we
    represent. This can be regarded as the base
    knowledge level
  • Representation of the facts which we manipulate.
    This can be regarded as the symbol level since we
    usually define the representation in terms of
    symbols that can be manipulated by programs.

37
Simple Representation
Musician Style Instrument Age
Miles Davis Jazz Trumpet 25
John Zorn Avant Garde Saxophone 35
Frank Zappa Rock Guitar 25
John Mclaughlin Jazz Guitar 47
  • Simple way to store facts.
  • Each fact about a set of objects is set out
    systematically in columns.
  • Little opportunity for inference.
  • Knowledge basis for inference engines.

38
Rules
  • The term production rule system refers to several
    different knowledge representation schemes based
    on the general underlying idea of
    condition-action pairs, which are also called
    if-then pairs, situation-action pairs, production
    rules, or just plain productions.
  • Production rule systems have been shown to be
    capable of modelling any computable procedure. On
    the surface a production rule resembles a
    predicate calculus implication statement. A
    production rule is written in the form
  • if this condition holds, then this action is
    appropriate.

39
Rules
  • (rule (name)
  • (if (trigger fact 1)
  • (trigger fact 2)
  • (trigger fact n))
  • (then (conclusion fact 1, or action 1 )
  • (conclusion fact 2, or action 2)
  • (conclusion fact n, or action n)))

40
Rules
  • if it is raining then the ground is wet
  • if height of X gt height of Y then X is taller
    than Y
  • where X and Y are variables, and the database has
    the following items
  • it is raining
  • the ground is wet
  • height of Tom 6
  • height of Tim 5
  • Tom is taller than Tim

41
Uncertainty
42
Reasoning
43
Homework 1
  • In a food processing plant the chief technician,
    Alf, is due to retire. He is the only one who
    understands how all the equipment works and how
    to repair it when faults occur. It is decided to
    attempt to capture the knowledge of this expert
    into a knowledge-based system. You are hired as
    the knowledge engineer, responsible for capturing
    this knowledge. You decide to approach the
    problem one step at a time, taking each piece of
    equipment in the plant in turn. Part of an
    interview session is shown below

44
  • YOUHow do you set about fixing a problem with
    the mixer?
  • ALF Well, the mixers are usually OK, so always
    check the feed pump first. If the feed coming in
    from the pump is OK, then check the mixer. Check
    the mixer temperature, if its above 20C then the
    cooling fan has gone and needs replacing. If its
    not that check the blades they may be broken and
    need replacing. If theyre OK it has to be the
    mixer output that is clogged, so check that. If
    the output is not clogged then it is beyond me,
    call in the manufacturers repair team.
  • YOU What happens if the problem is in the
    pump?
  • ALF If the problem is in the pump, check the
    fuel line first clear them if they are blocked
    that will fix it. If not check the pressure, if
    it is low replace the sealing washers on the
    pump. If none of this works then the feed mix
    coming through the pump is wrong and needs
    changing.

45
Questions
  • Create an expert system domain dictionary for the
    above problem.
  • Draw a spider diagram for the above problem.
  • Build decision tree (s) for the above system.

46
Homework2
  • a) Describe and show a pseudo-code of a hill
    climbing algorithm.
  •  
  • b) With regards to accepting moves, describe how
    differently Simulated Annealing and Tabu Search
    work.
  • c) What is the main difference(s) between
    simulated annealing and hill climbing?
  • d) What is the effect of having the starting
    temperature too high or too low in the cooling
    schedule.
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