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Artificial Intelligence.
Module Title
AI 401
Module Code
Subject Status
Credit Rating
Credit Level
Pre-Requisite Subjects
Co-requisite Subjects
Mandatory
10
4
None
None
Lecture
2 Hours per Week
Tutorial
Computer Lab
Other
1 Hour per Week
0
Total Contact Hours
Subject Aims
This subject aims to make students aware of the many areas of artificial intelligence and the tools
available for AI type solutions. Identify suitable problems for AI solutions. Examine in detail and
implement structures for representing knowledge. The manipulation of knowledge, especially rule
based systems. Examine the various types of expert system paradigms with examples and examine the
concept of expert system shells. Implementing some of the AI techniques that have been introduced
with the AI Programming Language Prolog. Natural Language Parsing.
Learning Outcomes:
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The student will be knowledgeable in the foundation and general
principles of Artificial Intelligence
The student will be aware of the types of AI solutions that can be
formulated.
The student will be able to formulate, prove and program logical
representations.
The student will be able to recognise structure and program various
knowledge representations.
The student will be able to represent and code search spaces and
solutions.
The student will be able to encapsulate knowledge in rule based
format.
The student will be knowledgeable in the representation and
manipulation of natural language.
The student will be able to program in a range of AI languages.
Syllabus Content
 Knowledge Representation
20%
 Represent and manipulate and program knowledge using
 Logic - Predicate calculus and lists, trees
 Rule based systems
 Semantic Networks
 Frames and scripts
 Neural Networks
 Game trees

Manipulation of Knowledge
20%
 Represent problem and search spaces.
 Represent and program search methods as a means of manipulating search spaces.
 Differentiate between various reasoning methods
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Natural Language Parsing
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15%
Interpret models for representing language content, canonical models etc.
Interpret Construct and program parse trees and parsing rules
Information retrieval and natural language parsing
Identify the limitations of natural language understanding by computers
Expert Systems
15%
Explain the design and structure of expert systems
Describe the features of expert system shells and tools E.G. CLIPS, COOL
Critique Knowledge base design issues and development.
Importance of explanation facilities.
Examination of classical and commercial examples
Hybrid Expert systems.
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AI Languages
30%
 Write Programs in Prolog to cover the following features
 Declaring and querying facts, rules, constants, variables,
structures, lists
Modifying the Database
Formatting queries
Evaluating goals with facts and rules
Tracing evaluations
 Implement AI problems using Prolog and CLIPS
 Represent real world problems and relationships as facts and rules in the program
database
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 Evaluate theorem proving using Backward chaining, binding and instantiation
 Implement knowledge representations using Prolog and CLIPS.
Achieving and Assessing the Attainment of Learning Outcomes
Learning Outcome
Teaching Methods
Assessment Method
1) The student will be
Lecture
Examination
Lecture
Examination, programming
assessment
Lecture
Examination
Lecture, Lab Work
Examination, programming
assessment
Lecture, Lab Work
Examination, programming
assessment
Lecture,
Examination, programming
assessment
Lecture, Lab Work
Examination, programming
assessment
Lecture, Lab work
Examination, programming
assessment
2)
3)
4)
5)
6)
7)
8)
knowledgeable in the
foundation and general
principles of Artificial
Intelligence
The student will be aware of
the types of AI solutions that
can be formulated.
The student will be able to
formulate, prove and program
logical representations.
The student will be able to
recognise structure and
program various knowledge
representations.
The student will be able to
represent and code search
spaces and solutions.
The student will be able to
encapsulate knowledge in rule
based format.
The student will be
knowledgeable in the
representation and
manipulation of natural
language.
The student will be able to
program in a range of AI
languages.
Method of Assessment:
Continuous Assessment
40%
Written examination
Various small Programming tasks over the year
Expert system Shell assignment incorporating NLP
Knowledge representation and manipulation Assignment
Final Examination
10%
10%
10%
10%
60%
Examples of assessments
Sample Simple Prolog Tasks
1) Write a predicate called extract_list which extracts lists from a list of elements containing lists and other
types.
2) Write a predicate called display_list which use the member predicate to list all the elements of a list each on
a separate line.
3) Write a predicate delete_lists that deletes (use the delete built in predicate in swi-prolog) all elements from a
list that are lists.
Expert system assessment
Using a diagnostic area that you are familiar with, design an expert system using the rule format to represent
rules in the problem domain you have chosen. Implement a meta-interpreter that will show how a conclusion
was reached, ask questions of the user and explain why that question is being asked. Write a natural language
interpreter using DCG rules that will handle simple expressions of fact and query in the context of the expert
system.
Knowledge representation and manipulation Assignment
Planning and search in Prolog
Build an intelligent Robot (Program) that can navigate the college (ground floor only).
Recommended Reading
Core Text
Title
Authors
Publisher
Year
Artificial Intelligence a modern
approach
Computational Intelligence
Russell and Norvig
2001
Programming in Prolog
The Essence of Artificial
Intelligence
Expert systems and applied
Artificial Intelligence
Bradko
Alison Cawley
Prentice Hall
ISBN 0-13-103805-2
Oxford
ISBN 0-19-510270-3
Addison-Wesley
Prentice Hall
Turban
MacMillan
1992
Authors
Publisher
Year
John Kelly
Covington, Nute & Vellino
Davalo, Eric and Naim,
Patrick
Waterman, D. A.
Callear, David
Prentice Hall
Prentice Hall
MacMillan Education
1997
1997
1991
Addison Wesley
DP Publications
1986
1994
Supplementary Reading
Title
The Essence of Logic
Prolog Programming in depth
Neural Networks
A Guide to Expert Systems
Prologue Programming for
Students
Poole, Mackworth, Goebel
1998
1991
1998
Teaching Methodology
Lectures will be given on the theoretical content of the course. Lab work will be completed on the application of
the theory presented. Assessment will be based on application of the theory to formulation of programmed
content.