Download Soran University Artificial Intelligence Module Specification 1

Soran University
Artificial Intelligence
Module Specification
1. Module Title – Artificial Intelligence
2. Module Code: CS407AI
3. Module Level - Fourth Stage
4. Module Leader – Michael Ulman
5. Teaching Semester – 7 & 8
6. Credit Rating for the module - 4 Credits
7. Prerequisites and co-requisites
None
8. Module Summary
This module covers the main aspects of classical AI and non-logical
engineering approaches to Artificial Intelligence. Starting with a history of AI
and considering philosophical questions about thinking machines and
consciousness. The module will then use the Agent based paradigm for
covering such classical topics as search, planning, logic, and knowledge
representation. Then covering specialist areas of AI such as: expert systems,
natural language processing, neural networks and genetic algorithms.
Practical work will involve implementing programs in these areas.
Assessment will involve exams and course work assignments.
9. Module Aims
The aim of the module is to give the student a broad introduction in theory and
practice to the main areas of classical AI as well as newer engineering
approaches such as neural networks and genetic algorithms. Specialist areas
such as experts systems, natural language processing are also explored.
Practical sessions will involve programming using relevant languages and
using expert system shells.
10. Learning Outcomes
On successful completion of this module students will:
LO1 Know the history of AI and the main philosophical discussions about AI
LO2 Understand the Agent paradigm in AI
LO3 Know how search and planning work, and the different types of search
and planning algorithms
LO4 Have an understanding of knowledge representation, including the use of
logic for this
LO5 To be able to write AI programs using an appropriate programming
language
LO6 To be able to create an expert system using an expert system shell
LO7 To understand specialist areas of AI such as Natural Language
Processing, Neural Networks, and Genetic Algorithms
11. Syllabus
History of AI
Philosophical Questions about AI – Turing and Searle
AI Agents
Search – breadth first, depth first, etc
Logical Agents
Propositional Logic
Predicate Logic
Knowledge Representation
Classical Planning
Expert Systems
Natural Language Processing
Neural Networks
Genetic Algorithms
Real World examples of AI
Writing AI programs and creating expert systems
12. Assessment Strategy
The Assessment strategy consists of monthly tests which will be held after
every 4 weeks of teaching. These exams will be both theoretical and practical
exams. Their aim is to encourage learning and assimilation of the content
during the course of the module and to provide feedback to the student and
lecturer as the students’ progress through the module.
Monthly Exam 1 (LO1, LO2, LO3, LO4)
Monthly Exam 2 (LO5, LO6, LO7)
There will be a mini-projects designed to test the ability of the students to
write AI programs using an appropriate programming language and also to
create an expert system using an expert system shell.
LO2, LO3, LO4 LO5, LO6
There will be Final Examinations at the end of the module, both a Theory
Exam and Practical Exam. This will assess the student’s understanding of
the content and its practical application.
Practical Exam (LO1, LO2,LO3, LO4, LO5, LO6, LO7)
Theory Exam (LO1, LO2, LO3, LO4, LO5, LO6, LO7)
13. Summary description of assessment items
Assessment Description
Type
of Item
Exam
MiniProjects
Final
Practical
Exam
Final
Theory
Exam
Formative
Monthly
Exam 1 and
2
(LO1,LO2,
LO3, LO4)
Coursework
Assignment
(LO1,
LO2,LO3,
LO4,
LO5,LO6)
Practical
Exam
(LO1,
LO2,LO3,
LO4,
LO5,LO6,
LO7)
Theory
Exam LO1,
LO2,LO3,
LO4,
LO5,LO6,
LO7)
Workshop
Practical
Exercises
%
Weightin
g
10 %
Grading
Tariff
Week due
%
5
30 %
%
10
20
%
End of the
module
40
%
End of the
module
0%
Informal
Feedback
Weeks 1
to 10
14. Learning Session Structure
2 hour lecture
2 hour workshop
15. Learning and Teaching Methods
Lecture – 2 hours per week (32 hours) – to convey the knowledge content of
the module and to engage the students in a dialogue to stimulate their
thinking and access their understanding of the material
Workshop – 2 hours per week (32 hours) - the workshop will consist mainly
of practical exercises relating to the practical application of the theory
content, in particular AI programming and design and creation of an expert
system using an expert system shell
16. Scheme of Work
Week
Delivery Content
Method
1
Lecture, History of AI
Philosophical
questions
1
Lab
Turing Test,
Searle’s Chinese
Room
experiment
2
Lecture
Intelligent
Agents
2
Lab
Agent programs
Learning
Materials
Slides1,
Learning
Form of
Outcomes Assessment
LO1
Formative
Assessment
Exercises
1
LO1
Slides2,
LO2
Exercises
2
Slides3,
LO5
Exercises
3
Slides4,
LO3, LO5
LO5
3
Lecture
3
Lab
4
Lecture,
Introduction to
Search
Search
programs
More Search
LO3
4
Lab
programming
5
Lecture,
Logical Agents
Exercises
4
Slides5,
5
Lab
Lecture,
Exercises
5
Slides6,
LO4, LO5
6
Logic
programming
Predicate Logic
6
Lab
Rule based
programming
Exercises
6
LO4, LO5
LO3
LO4
LO4
Formative
Assessment
Formative
Assessment
Formative
Assessment
Formative
Assessment
Formative
Assessment
7
Lecture,
7
Lab
8
Lecture
8
Lab
Classical
Planning
Planning
programs
Knowledge
Representation
programming
9
Lecture,
Expert Systems
9
Lab
10
Lecture,
10
Lab
CLIPS expert
system shell
Expert Systems
– Knowledge
Engineering
More CLIPS
Slides7
LO3
Exercises
7
Slides8
LO3, LO5
Exercises
8
Slides9,
LO4, LO5
Exercises
9
Slides10,
LO6
Exercises
10
LO6
LO4
LO6
LO6
LO6
11
Lecture
11
Lab
12
Lecture
12
Lab
13
Lecture
13
Lab
14
Lecture
14
Lab
15
Lecture
15
Lab
16
Lecture
16
Lab
Expert Systems
– CLIPS project
CLIPS project
Natural
Language
Processing
NLP/project
work
Neural Networks
NN Exercises/
project
More Neural
Networks
NN Exercises/
project
Genetic
Algorithms
GA Exercises,
project
Real world AI
projects
Project work
Formative
Assessment
Formative
Assessment
Formative
Assessment
Formative
Assessment
Formative
Assessment
Slides 11
Project
work
Slides 12
LO6
Exercises/
project
Slides 13
LO6, LO7
Exercises/
project
Slides 14
LO6, LO7
Exercises/
project
Slides 15
LO6, LO7
Exercises/
project
Slides 16
LO6, LO7
Project
LO6
LO7
LO7
LO7
LO7
LO7
Formative
Assessment
Formative
Assessment
Formative
Assessment
Formative
Assessment
Formative
Assessment
17. Bibliography
Artificial Intelligence: A Modern Approach 3rd edition Peter Norvig, Stuart
Russell
Paradigms of Artificial Intelligence Programming: Case Studies in Common
Lisp, Peter Norvig
Common Lisp:A gentle introduction to symbolic programming, David S.
Touretzsky
The Cambridge Handbook of Artifical Intelligence, eds Keith Frankish &
William M. Ramsey
Introduction to Neural Networks, Kevin Gurney
Minds, brains and programs John R. Searle
Computing Machinery and Intelligence, A M. Turing
CLIPS Reference Manual
CLIPS User’s Guide, Joseph C. Giarratano
18. Authored by
[
Michael Ulman
10/11/14
19. Validated and Verified by