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Name of Course/Module
Course Code
Status of Subject
MQF Level/Stage
Note :
Certificate – MQF Level 3
Diploma – MQF Level 4
Bachelor – MQF Level 6
Masters – MQF Level 7
Doctoral – MQF Level 8
Version
(state the date of the last Senate approval)
Pre-Requisite
Name(s) of academic/teaching staff
Artificial Intelligence
TIC3151
Elective
Bachelor Degree – MQF Level 6
Date of New Version : Year 2012
None
Prof. Yashwant Prasad Singh
Dr. Lee Chien Sing
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Semester and Year offered
Trimester 1 (Beta Level)
Objective of the course/module in the programme :
The course aims to introduce the principles and methods in artificial intelligence as technical subject
and its limitations, by working through numerous examples of how, when, and where to apply AI
techniques. It also exposes the students to the search techniques, logic, knowledge representation
techniques and alternate ways of representing knowledge and explore the consequences of the
various representations in AI problem solving
Subject Learning Outcomes :
By the end of the subject, students should be able to
 Explain about intelligence, artificial intelligence and various AI-techniques
 Demonstrate a good understanding in agent technology,
 Ability to identify problems that can be expressed in terms of search problems or logic
problems, and translate them into the appropriate form, and know how they could be
addressed using an algorithmic approach.
Ability to identify problems that can be expressed in terms of appropriate learning methodology for the
problem area.
Synopsis:
To familiarize with several problem-solving techniques, mainly search and logic. Problem solving in
artificial intelligence is done using LISP and logic programming.
Memperkenalkan beberapa teknik penyelesaian masaalah, terutamanya, masalah gelintaran and
logik. Penyelesaian masaalah dalam bidang kecerdasan buatan dilaksanakan dengan mengunakan
pengaturcaraan logic programming dan LISP.
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Mapping of Subject to Programme Outcomes :
Programme Outcomes
Apply soft skills in work and career related activities
To make use of fundamentals concepts and formulate best practices.
Apply technical concepts and practices in specialized areas of Computer Science
Analyze the requirements to address problems or opportunities faced by
organizations
Recognize and pursue continued life-long learning throughout their career
Blend innovative mind and entrepreneurial skills
Relate moral values and professional ethics to the practice of an ICT professional.
Assessment Methods and Types :
Description/Details
Method and Type
Coursework:
Midterm test
Quizzes and Assignment
% of
Contribution
5
30
20
30
5
5
5
Percentage
30%
30%
Final Exams
40%
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Details of Subject
Topics
Introduction
History and definition of Artificial Intelligence; AI problems and
problem spaces: Game Playing; Planning; Understanding; Natural
Language Processing; Parallel and Distributed AI; Learning;
Connectionist Model; Common Sense; Expert Systems; Perception
and Action; Fuzzy Logic; Neural Network.
Intelligent Agents
Agent and Environments; Good Behaviour: Concept of Rationality;
The Nature of Environments, The structure of agents
Problem
Solving
Solving Problems by Searching; Problem-Solving Agents; Example
Problems; Searching for Solutions; Uninformed Search Strategies;
Searching with Partial Information; Informed Search and Exploration;
Informed (Heuristic) Search Strategies; Heuristic Functions; Local
Search Algorithms and Optimization Problems; Game Playing;
Constraint Satisfaction Problems (CSPs) and backtracking search for
CSPs.
Knowledge
and
Reasoning
I
Knowledge Representation: Logical Agents; Agents based on
Propositional logic; The meaning of knowledge; Semantic Nets;
Difficulties with Semantic Nets; Object-Attribute-Value Triples;
Frames; Difficulties with Frames; Logical Agents;
Knowledge and Reasoning II
First- Order Logic; Inference in First-Order Logic; Propositional vs.
First-Order Inference; Inference rules for quantifiers; Reduction to
propositional inference; A first-order inference rule; Unification; Firstorder definite clauses; Resolution; Conjunctive normal form for firstorder logic; The resolution inference rule; Example proofs;
Completeness of resolution; Resolution strategies
Acting Locally
Planning; A Simple planning agent; From problem solving to
planning; Planning in Situation Calculus; Basic representations for
planning; A partial-order planning example; A partial-order planning
algorithm; Planning with partially instantiated operators.
Learning
Forms of Learning; Inductive Learning; Learning Decision Trees;
Decision trees as performance elements; Expressiveness of decision
trees; Inducing decision trees from examples; Choosing attribute
tests; Assessing the performance of the learning algorithm; Noise and
over-fitting; Broadening the applicability of decision trees; Ensemble
Learning
and
Computational
Learning
Theory.
Mode of Delivery
Lecture
Tutorial/Lab
2
2
2
2
8
8
4
4
4
4
4
4
4
4
Total
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16.
28
28
Lab / Tutorial :
 Learn LISP Programming for AI Problem Solving
 Apply different search techniques (e.g. DFS, BFS and Heuristic search) to solve a given problem.
 Learn about First-Order Predicate Logic, Game playing and/or Machine learning
Total
Student
Face to Face
Total Guided and Independent Learning
Learning Time (SLT)
(Hour)
17.
18.
Lectures
Tutorials + Quiz
Midterm test (1)
Assignment (1)
28
28
1
0
28
28
6
10
Quizzes
1
4
Final exam (1)
2
16
SUBTOTAL
60
106
Total SLT
Credit Value
Reading Materials :
Textbook
J. Russell and Peter Norvig ; “Artificial
Intelligence: A Modern Approach “, 2nd
Edition., 2003.
179/40 = 4
Reference Materials
Bratko, I., "Prolog programming for Artificial
Intelligence", 3rd Edition, Addison-Wesley, 2000.
T. Dean, J. F. Allen & Y. Aloimonos, Thomas
Dean, and James Allen, and Yiannis Aloimonos,
"Artificial Intelligence: Theory and Practice",
Benjamin Cummings, 1995.
George F. LUGER; “Artificial Intelligence:
Structure and Strategies For Complex Problem
Solving; 6th Edition, PEARSON, Addison
Wesley2009
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StrategiesUCTURE AND STRATEGIES FOR
Complex Problem Solving
Appendix (to be compiled when submitting the complete syllabus for the programme) :
1. Mission and Vision of the University and Faculty
2. Mapping of Programme Objectives to Vision and Mission of Faculty and University
3. Mapping of Programme Outcome to Programme Objectives
4. Progarmme Objective and Outcomes (Measurement and Descriptions)