Computer Science

IT00097 / IT08X97:
Artificial Intelligence
Learning Guide 2014
ACADEMY OF
COMPUTER SCIENCE
AND SOFTWARE ENGINEERING
FACULTY OF SCIENCE
Professor EM Ehlers
Head of Department
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[ IT00097 / IT08X97 ] Artificial Intelligence – 2014






Please note
The most up-to-date and correct version of this
Artificial Intelligence Learning Guide
is available electronically on EVE.

In the event of any differences between this copy and the copy
currently on EVE, the student is to default to the version
available on EVE.
Last updated: 2014/02/03 10:48:27 AM



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Contents
1. Problem Solving ......................................................................................................................... 1
2. The Module ................................................................................................................................. 1
2.1. Purpose of Module ............................................................................................................ 2
2.2. Module Outcomes & Module Assessment Criteria ........................................................... 2
2.3. Module Resources ............................................................................................................. 3
3. Lectures ...................................................................................................................................... 3
4. Lecturer ...................................................................................................................................... 3
5. Scheduled Programme ............................................................................................................... 4
6. Assessments ................................................................................................................................ 4
6.1. Class Tests ......................................................................................................................... 5
6.2. Intelligent Agent Project ................................................................................................... 5
6.3. Semester Test .................................................................................................................... 7
6.4. Examination ...................................................................................................................... 7

THIS GUIDE IS TO BE USED IN CONJUNCTION WITH THE ACADEMY’S
GENERAL LEARNING GUIDE FOR HONOURS MODULES
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1. Think!

In this module, students are introduced to the principles, concepts
and a number of sub-disciplines of AI.
Concepts include agents, search strategies, state spaces and
knowledge representation with sub-disciplines that may include:
 Agent technology
 Robotics
 Machine learning
 Computer vision
 Multi-agent systems
 Natural language understanding
 Planning
 AI and education
 Reasoning under uncertainty
 Intelligent technologies for information retrieval
 Knowledge mining
Students should note that successful completion of this module
requires a strong willingness to conduct research independently
and good knowledge of programming in order to successfully implement an intelligent system that
contributes to the greater bulk of the module’s mark composition.

2. The Module
Module name: Artificial Intelligence
Prerequisites for module:
1. To be admitted to any honours module, the student
must have an average of at least 60% in his/her final
year of study for Computer Science/Informatics.
2. Knowledge of a suitable programming language is
required as practical work will entail the
implementation of an intelligent agent-based system.
Module NQF level: 8
NQF Credits:
(calculated according to notional hours)
14
Duration of Module:
(Weeks/Semester)
14 weeks
Type of Module: Semester 1 Module
Language of Delivery: English
About this learning
guide This learning
guide contains details
specific to the
Artificial Intelligence
module and should be
used in conjunction
with the Academy of
Computer Science &
Software
Engineering’s General
Learning Guide for
Honours Modules.
Differences
between this
learning guide and
the general
learning guide?
Students are to defer
to this module specific
learning guide.
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2.1. Purpose of Module
The module introduces students to important concepts for implementing Artificial Intelligence in
Information Technology systems. A number of fields in Artificial Intelligence will also be discussed.
2.2. Module Outcomes & Module Assessment Criteria
MODULE OUTCOMES MODULE ASSESSMENT CRITERIA
At the end of this module the student should be
able to do the following:
The student will be assessed as competent if:
1. Discuss what Artificial Intelligence is and
the role of Artificial Intelligence in
Information Technology and society as a
whole.

o Artificial Intelligence is defined precisely.
o The role of Artificial Intelligence in
Information Technology and society as a
whole is sufficiently described.

2. Describe the concepts and theories related
to Artificial Intelligence.

o Fundamental concepts and theories related
to Artificial Intelligence are stated exactly.
o A comparison of different concepts and
theories related to Artificial Intelligence are
given logically in an essay and/or
application to a case study.
3. Research, design and implement an
intelligent agent-based system.

o Sufficient background research on
intelligent agent-based systems is logically
assembled.
o A detailed design of an intelligent agent-
based system is proposed.
o A complete operational prototype of an
intelligent agent-based system is produced.
4. Identify the components needed for an
intelligent agent-based system.

o Key components needed for an intelligent
agent-based system are correctly identified.
o Thorough knowledge of the key
components needed for an intelligent
agent-based system is expressed precisely
in an essay and/or in an application to a
case study.
5. Demonstrate exposure to Artificial
Intelligence principles.

o Artificial Intelligence principles that will
facilitate the application of AI techniques in
Information Technology are correctly
chosen.
o A sufficient exposure to selected Artificial
Intelligence principles is logically applied
in an essay and/or in an application to a
case study.
6. Apply skills to manage, organize and
deliver research results.

o The necessary skills to manage and
organize research results are correctly
applied in a practical/research project.
o Research results are logically delivered
individually and/or in a team relation.
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2.3. Module Resources
A blended learning approach that makes use of the following teaching/learning methodology
opportunities and experiences is used:
 Lectures
 Module website (http://eve.uj.ac.za)
 Microsoft DreamSpark Premium Software (http://eve.uj.ac.za/ds).
 Using books and subject-related periodicals
 Consultations with module lecturer
2.3.1. Prescribed Textbook
The following book by authors Russell & Norvig is prescribed for the module. It is important that
students acquire a copy of the text as no lecture notes will be provided.


Title: Artificial Intelligence: A Modern Approach
Edition: 3
rd

Author: Russell, S & Norvig, P
Publisher: Prentice Hall
ISBN-13: 978-0-13-604259-4

The authors of the book also maintain a website supporting the book by providing updates and links
to concepts in Artificial Intelligence at http://aima.cs.berkeley.edu/index.html.

3. Lectures
Artificial Intelligence lectures take place weekly every Monday from 15h50 to 17h25 in C Les 301.
The attendance of lectures is compulsory and students with a 25% or higher absence rate may be de-
registered from the module.

4. Lecturer
Artificial Intelligence is offered by Professor Ehlers (E-Ring 210). Students seeking consultation with
the lecturer will be required to send an e-mail ([email protected]) to make an appointment.

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5. Scheduled Programme
The following schedule is subject to change. Students should however note that a public holiday on
21 April 2014 means that there are only effectively 13 lectures in the module this year. Each lecture
is an opportunity for a class test and students are encouraged to prepare for each lecture with this
mind-set.
Week Date Day Subject(s) Covered / Overview
01 2014/02/03 Mon Introduction to Artificial Intelligence (Chapter 1)
02 2014/02/10 Mon Intelligent Agents (Chapter 2)
03 2014/02/17 Mon Solving Problems by Searching (Chapter 3)
04 2014/02/24 Mon Beyond Classical Search (Chapter 4)
05 2014/03/03 Mon Adversarial Search (Chapter 5)
06 2014/03/10 Mon Constraint Satisfaction Problem (Chapter 6)
07 2014/03/17 Mon Logical Agents (Chapter 7)
Recess 1
08 2014/03/31 Mon Knowledge Representation (Chapter 12)
09 2014/04/07 Mon Robotics (Chapter 25)
10 2014/04/14 Mon Semester Test
11 2014/04/21 Mon Public Holiday: Family Day – No lecture
Recess 2
12 2014/05/05 Mon Specialised Topic
13 2014/05/12 Mon
Philosophical Foundations (Chapter 26)
AI: Present & Future (Chapter 27)
14 2014/05/19 Mon Project Demonstrations

6. Assessments
An integrated approach to assessment whereby assessment forms an integral part of teaching and
learning is followed:
 Formative Assessment - students are assessed throughout the semester in the form of a minimum
of one class test and/or assignment which comprises 10% of the semester mark. Students are also
required to submit a document detailing the design of their project agent which makes up 15% of
the semester mark. The actual implementation of the agent carries a weight of 50% of the
semester mark whereas the student’s submission of a final report on the implemented agent
weighs 15%. The final 10% of the semester mark is contributed by the student’s semester test.
 Summative Assessment – a two hour written examination that is representative of all the work
covered is written at the end of the semester.
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To pass the Artificial Intelligence module, students will need to successfully complete a number of
assessment opportunities. The listing of each assessment opportunity and their weight towards the
Module / Semester mark is presented below.

Assessment Counts Towards Counts Towards
Semester Test 10 %
50 %

At least 1 class test and/or assignment 10 %
1 Agent Design Document 15 %
1 Implementation of an Agent 50 %
1 Final Agent Documentation 15 %
Examination 50 %

In addition to requiring a 50% Final Mark to pass the module, students are also reminded of the
following additional requirements:
 A minimum of 40% for the Module / Semester is required to gain entrance to write the
Examination.
 A minimum of 40% must be obtained in the Examination.
Students should also take note that 80% of the Module / Semester mark is attributed to
assessment opportunities that involve research. This is to comply with the requirement that all
Honours-level modules should include a minimum of 25% research in the Final Mark.
6.1. Class Tests
Each lecture presents an opportunity for a class test. Students should therefore be prepared to write
class tests that will count towards the semester mark.
6.2. Intelligent Agent Project
Students are required to implement an intelligent agent-based system which will be demonstrated
to the lecturer towards the end of the semester. It is important to stress that learners be aware of the
significant weight of the intelligent agent-based system project’s contribution to the semester mark.
The intelligent agent-based system project is also to be completed on an individual basis and consists
of a number of deliverables, the criteria of which are detailed in the following subsections.
Module /
Semester
Mark
Final
Mark
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6.2.1. Deliverable 1: Agent Design
Criteria Marks
Background 10
Classification of Agent (what kind of agent is it?) 10
Use Cases and Use Case Diagrams 10
Use Case Descriptions 10
Class Diagrams 20
Communication Diagrams 10
Interaction Sequence Diagrams 10
Formatting, Document, Content 10
Intelligence of Agent 10
Total 100
6.2.2. Deliverable 2: Agent Final Documentation
Criteria Marks
Critical evaluation of implementation 10
Critical evaluation of design 10
Critical evaluation of intelligence of system 10
Future improvements 10
Presentation of documentation 10
Total 50
6.2.3. Deliverable 3: Agent Implementation
Criteria Marks
Presentation (Introduction of Agent) 10
Problem Complexity 10
Successful demonstration of agent (achievement of goals) 50
Interface 10
Implementation of Sensors & Actuators (Percepts) 10
Knowledge Representation 10
Size of solution system 5
Methodologies (use of search trees, etc) 20
Application of agent in real-world scenario 5
Intelligence of agent (is it REALLY intelligent?) 10
Innovation 5
Use of correct implementation tool 5
Total 150
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6.3. Semester Test
Students will write a semester test on 14 April 2014 during the lecture period. Further details (such
as scope), will be provided during the course of the module.
6.4. Examination
The examination is a 2-hour theory paper worth 100 marks which can test on all work that was
covered during the course of the semester which is not limited to the lectures, but should include the
research assignment and project.
A summary of the examination information is provided below.
Marks: 100
Duration: 2 hours
Scope: All work covered during the Semester
Type: Theory
Date, Time & Venue: To be announced by the Academy on Eve and on the Departmental
Noticeboard