Table 3.3j: Summary of information on MITM613
1.
Name of Course: Intelligent Systems
2.
Course Code: MITM613
3.
Name(s) of Academic Staff: Dr. Abdul Rahim Ahmad
4.
Rationale for the inclusions of the course in the programme:
1. To provide understanding of intelligent systems, the methods and the tools for implementing
Intelligent Systems.
2. To enable students to compare the pros and cons of each method of Intelligent Systems.
3. To enable students to design Intelligent system based on methods learned.
4. To develop the ability to implement an intelligent system based on methods learned.
5.
Semester and Year offered: Semester 2
6.
Total Students Learning Time (SLT)
182 hours
L= Lecture
T= Tutorial
P= Practical
O= Others
Face to Face
Total
L
T
P
O
12
X
14
0
14
0
182
7.
Credit value: 3
8.
Prerequisite (if any): N/A
9.
Objectives:
Upon completion of programme, students should be able to:
1. Explain the various methods of implementing Intelligent systems
2. Describe the issues involved in each method of implementing an Intelligent System.
3. Describe the tools that can be used.
4. Develop a particular intelligent system of choice in a class project environment.
10.
Learning Outcomes:
1. Define the terminologies commonly used in Artificial Intelligence (AI) and Intelligent Systems
2. Describe the different methods of AI and Intelligent Systems namely the knowledge base
system and the computational learning systems.
3. Analyze existing knowledge based system and computational learning system.
4. Design knowledge based system and / or learning system such as expert system and
prediction system.
5. Use various tools for implementation and development of knowledge based system and / or
learning system.
6. Implement an expert system by building the knowledge base and the inferencing engine
using Prolog programming language and implement a prediction system using methods such
as neural network or Support vector machine.
11.
Transferable skills:
1. Decision making skill
2. Synthesis skills
3. Effective communication skill
4. Critical and analytical skills
5. Qualitative and Quantitative skills
12.
Teaching-learning and assessment strategy
Overall in the course, students will be exposed to various aspects of artificial intelligent system. As
Artificial Intelligent (AI) itself meant differently to different people, depending on their field of expertise
and study, students will be introduced to all these different ideas and opinions. Generally, most
people in AI agreed to the division of AI into knowledge based (where intelligence are entered or built
into the system by gathering the information from experts) and the computational intelligent system
(where intelligence are computed into a model based on a lot of past data available). Students will be
given papers to read and compare the methods that have been used so far and then write up reports
for the assignments. Emphasis is put on experiential learning and interactive discussions where
students need to present their findings in class. The course is intended to be theoretical as well as
practical. To gain some practical knowledge, students are required to produce prototype systems for
knowledge based and computational based AI.
In completing the course assignment and tasks, students are required to work individually and/or as
a group to be involved in problem-solving and making decisions. Students’ knowledge and ability are
also tested in case studies, projects, interactive discussions, mid-term examination and final
examination.
13.
Synopsis
This course emphasises on the methods and tools that can be used to develop intelligent systems.
The tools can be divided into knowledge-based tools, computational intelligence tools and a hybrid of
both. Knowledge based systems include expert and rule-based system, object-oriented and framebased systems and intelligent agents. Computational intelligence includes neural networks, genetic
algorithms and further optimization algorithms. Fuzzy logic, a technique which can handle
uncertainties is a hybrid of both. Intelligent systems enabled a range of problems to be tackled more
effectively.
14.
Mode of delivery:
Lectures, interactive discussions and system development, presentation.
15.
Assessment Methods and Types
Assignments
Project
Mid Semester exam
Final examination
15
15
30
40
100
Assessment
methods
Final examination
MidSemester
Assignment
Project
CO
1
X
X
X
X
CO
2
X
X
X
X
CO
3
X
X
X
X
CO
4
X
X
X
X
CO
5
X
CO
6
X
CO
7
X
CO
8
X
X
X
X
X
X
X
X
X
16.
Mapping of the course to the Programme Outcomes (PO)*
1- Slightly
2- Moderately
3- Substantive
Course Outcomes (CO)
PO1 PO2 PO3 PO4 PO5
1. Define the terminology commonly
2
2
1
3
used in Artificial Intelligence (AI) and
Intelligent Systems
2. Describe the different methods of AI
2
2
1
2
and Intelligent Systems namely the
knowledge base system and the
computational learning systems.
3. Analyze existing knowledge based
2
2
1
2
system and computational learning
system.
4. Design knowledge based system and
2
2
3
3
/ or learning system such as expert
system and prediction system.
5. Use various tools for implementation
2
2
2
3
and development of knowledge based
system and / or learning system.
6. Implement an expert system by
2
2
2
1
2
building the knowledge base and the
inferencing engine using Prolog
programming language
7. and implement a prediction system
2
2
3
3
2
using methods such as neural
network or Support vector machine.
PO6
PO7
PO8
2
2
3
1
1
1
3
2
2
2
2
1
*Please refer to:
Appendix I – Programme Outcomes 1 – 10
Appendix II – Program Educational Outcomes 1 – 4
Appendix III – CO-PO Matrix
17.
Mapping of the course to the Programme Educational Outcomes (PEO)*
1- Slightly
2- Moderately
3- Substantive
PEO 1 PEO 2 PEO 3 PEO 4
Course Outcomes
1. Define the terminology commonly used in
3
2
3
3
Artificial Intelligence (AI) and Intelligent
Systems
2. Describe the different methods of AI and
3
2
2
3
Intelligent Systems namely the knowledge
base system and the computational
learning systems.
3. Analyze existing knowledge based system
3
2
2
3
and computational learning system.
4. Design knowledge based system and / or
3
2
2
3
learning system such as expert system
and prediction system.
5. Use various tools for implementation and
3
2
2
3
development of knowledge based system
and / or learning system.
6. Implement an expert system by building
3
2
2
3
the knowledge base and the inferencing
PEO 5
PEO 6
1
1
2
3
2
1
2
3
1
1
2
3
engine using Prolog programming
language
7. Implement a prediction system using
methods such as neural network or
Support vector machine.
3
2
3
3
2
*Please refer to:
Appendix I – Programme Outcomes 1 – 10
Appendix II – Program Educational Outcomes 1 – 4
Appendix III – CO-PO Matrix
18.
Content outline of the course and the SLT per topic
Week
Topics
1. Introduction to Intelligent Systems
1
·
Knowledge-based systems and computational
intelligence
·
Expert and rule-based systems
·
Intelligent agents
·
Neural networks, genetic algorithms
·
Further optimization algorithms
·
Fuzzy logic
2.
Rule-based Systems
· Rules and facts, rule examination and rule firing
·
Consistency and assumptions
·
Forward chaining
·
Conflict resolution
·
Backward chaining
Refer Hopgood
Uncertainty
Bayesian updating
Certainty theory
Possibility theory: fuzzy sets and fuzzy logic
Refer Hopgood
·
·
·
Intelligent Agents
Agents and objects
Agent architectures
Multi-agent system
Refer Hopgood
·
·
·
Symbolic Learning
Learning by induction
Case based reasoning
Refer Hopgood
·
·
Soft Computing
Neural Network
Support Vector Machine
Other soft computing methods
Refer Hopgood
·
·
·
Hybrid Systems
Genetic-fuzzy systems
Neuro-fuzzy systems
Genetic neural systems
Refer Hopgood
·
·
·
Tools and languages
Refer Hopgood
3.
4.
5.
6.
7.
8.
Notes/References
Refer Hopgood
1
·
·
·
9.
Expert System shell
Toolkit and libraries
AI Languages
Current Trends and Issues in Intelligent Systems
·
Discussion on local and international researches on
Intelligent Systems based on Journal papers
·
Application areas
·
Technologies used
·
Problems faced
·
Proposed solutions
10. Case Study/Project
·
Proposal and report writing or Project on simulation
model of a simple intelligent system
19.
Main references supporting the course
 Adrian A. Hopgood, Intelligent Systems for Engineers and Scientists, 2nd Edition, CRC
Publication (2000).
Additional references supporting the course
 Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, 2nd Edition, Addison
Wesley (2004).
 Selected papers and journal articles used for assignment, analysis, classroom discussion and
case study purposes.
20.
Other Additional Information