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Title: Introduction to Artificial Intelligence, Soft Computing, A Case Study and Future Implication


1
Introduction to Artificial Intelligence, Soft
Computing, A Case Study and Future Implication
  • by
  • K. Lavangnananda
  • School of Information Technology (SIT)
  • King Mongkuts University of Technology Thonburi
    (KMUTT)

Sunday 19th October 2008. Graduate School of
Computer Assumption University
2
Definitions of Artificial Intelligence (AI)
  • The study of mechanisms that think and act like
    humans
  • -------------------------------------------
  • The study of mechanisms underlying intelligent
    behaviour through the construction and evaluation
    of artifacts that enact those mechanisms

3
  • Is
  • machine intelligence
  • possible ?

4
Concepts/Definition of Intelligence
5
Can machine think ?
6
Introduction to Soft Computing / Computational
Intelligence
  • Many believe that this is a modern approach to
    AI.
  • At present, there is no precise definition of
    these terms.
  • However, techniques in Soft Computing /
    Computational Intelligence are
  • Fuzzy Logic
  • Evolutionary Computation
  • Neural Networks
  • (Probabilistic Reasoning ?)

7
An example of Evolutionary Computation in
Knowledge Discovery (a data mining program SARG)
  • SARG is an acronym for Self-adjusting Association
    Rules Generator
  • An evolutionary computation system known, based
    on genetic programming known as Self-adjusting
    Association Rules Generator (SARG) was
    implemented.
  • SARG comprises 3 main components
  • Data preprocessing
  • Evolutionary computation
  • Final rule builder

8
  • Data preprocessing
  • The data set must be split into 2 sets, training
    set and test set.
  • Evolutionary Computation

9
Evolutionary computation
10
Final Rule Builder The format of the final
classification rule is IF condition(s) for the
rule with highest fitness value THEN (class
category of the rule with highest fitness
value) ELSE IF condition(s) for the rule with
2nd highest fitness value
THEN (class category of the rule with 2nd
highest fitness value) . ELSE IF
condition(s) for the rule with lowest fitness
value THEN (class
category of the rule with lowest fitness
value) ELSE (sample is unclassified)
11
An Example Predicting M.Sc. IT students GPA
  • The range of GPA is between 0 and 4.
  • After detailed analysis of student files,
    eight measurable attributes were considered
    relevant in judging whether an applicant should
    be admitted to the programme. These are shown in
    the following table.
  • Degrees of success (i.e. GPA expected) were
    classified into 3 categories
  • gt 3.5 (Class 1)
  • 3.0 - 3.5 (Class 2)
  • lt 3.0 (Class 3)

12
(No Transcript)
13
Datasets used
  • Data set available consisted of 276 past student
    records. They were taken from past student files
    from semester 2/1996 to semester 1/1999.
  • Training set consisted of 200 samples while 76
    samples were set aside for testing.
  • After numerous experiments, SARG yielded the
    best performance of 81.16 accuracy and produced
    6 rules.

14
Points to note
  • The limited number of samples available for
    training and testing may be crucial the task
    may be too difficult if sufficient number of
    samples is not available for training
  • The maximum number of conditions allowed in a
    rule has a direct influence on performance The
    maximum number in this work was set to 3 to avoid
    rules becoming to specific to the training set.
    This may be insufficient too. However, setting
    this number higher will make the task of
    generating rules much harder since more and
    longer chromosomes will be required as well as
    more iterations for each number of conditions.
  • Quality of attributes is another crucial factor
    Selecting relevant attributes requires careful
    analysis indeed. In this work, quality attributes
    such as no. of hours spent on revision each
    week and relevant experience cannot be
    obtained easily or almost impossible to assess.

15
Future implication of AI
  • Computer and IT technology have come to the
    point that the improvement and the future
    potential of machines and devices do not lie in
    their ability to do mundane tasks or processing
    data. People are, more and more, expecting these
    machines and devices to perform some decision
    making.

16
The above can be translated to the need to
program computing devices not what to do but how
to do. AI is such a discipline that provide the
basis for this need. Hence, the success in
fulfilling the requirement of future computing
devices lies in the success in AI research.
17
Future implication of AI
  • The benefit from intelligent machines are
    plentiful. They can assist, or even replace human
    in performing tasks which require intelligence.
    The ultimate goal of AI was clear from the
    beginning. It was meant to improve the quality of
    human life and to the betterment of society as a
    whole.
  • However, the implication of AI is not quite as
    clear as its goal. This has been an on-going
    debate for sometimes. The main issue is to what
    extent should human allow the decision making
    process to machine ?
  • .

18
So far, the impact of AI has far more positives
than negatives.
19
Thank you for your attentionQuestions are welcome
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