Title: Introduction to Artificial Intelligence, Soft Computing, A Case Study and Future Implication
1Introduction 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
2Definitions 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 ?
4Concepts/Definition of Intelligence
5Can machine think ?
6Introduction 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 ?)
7An 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
9Evolutionary computation
10Final 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)
11An 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)
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13Datasets 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.
14Points 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.
15Future 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. -
16The 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.
17Future 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 ? - .
18So far, the impact of AI has far more positives
than negatives.
19Thank you for your attentionQuestions are welcome