CSCI3006 Fuzzy Logic and Knowledge Based Systems (AI)

This web page is designed by Bob John to provide help and assistance in managing the module CSCI3006 Fuzzy Logic and Knowledge Based Systems (AI). It is a 'live' resource that we expect students to use to help improve their understanding in Artificial Intelligence and as a first point of call with any queries about the module. If you have any suggestions for improvement of the pages (omissions, things you'd like to see, useful web resources,errors) please email me.

Breaking news and information is presented HERE!

Here is the 2002/3 assignment

The jobs market is opening up for AI people. I put the odd one I see here to give you a flavour.

Some solutions for the example exam questionshere

Course Details

This module deals with, arguably, the two most successful techniques in the area of artificial intelligence. Fuzzy logic is a technique for modelling uncertainty and imprecision and appears in many applications for example in consumer products such as washing machines and camcorders. The ideas behind fuzzy logic use the notion that the world is not precise and that the ability to model words like hot, tall and expensive is very difficult using conventional mathematical techniques. The student will gain an understanding of fuzzy sets and how these are used in systems that contain fuzzy if-then rules for decision making. Knowledge Based Systems (KBS - also known as expert systems) are the most commercially successful technique in AI. These systems are analogous to data base systems except instead of storing data the use knowledge from an expert(s) to make decisions. Students will learn about the history of KBS as well as the various aspects of KBS development such as knowledge acquisition, inferencing, knowledge representation and system development. In summary, this module exposes the students to two highly successful methods for modelling expertise in decision-making environments.

Learning Outcomes:

On successful completion of this module a student will be able to:
1. understand and implement a basic fuzzy system (exam and coursework)
2. appreciate some theoretical issues of fuzzy logic and knowledge based systems (exam)
3. understand and implement a variety of fuzzy logic paradigms (exam and coursework)

Learning and Teaching Strategies

Technical material is formally presented via a series of lectures and tutorials supported by supervised laboratory work. Tutorials will reinforce the theoretical aspects of the module covered in lectures, consolidated by exercises and discussions.
Laboratory sessions will allow students to gain practical experience of the theoretical concepts covered in lectures. This will involve studying demonstrations and implementing simple exemplars (using packages or similar).

Lecture Plan and Assessment (pdf)

Here!

Notes (pdf) (But there will be other material given only in the lectures!!)

Here!

Lab Notes

Weeks 1 & 2

Here in Word

Weeks 3 to 5

Here in Word

Weeks 6 onwards

Here in Word

The assignment

Can be downloaded here

The members of staff teaching the course are:

Fuzzy Logic - Bob John email: rij@dmu.ac.uk
Knowledge Based Systems - Peter Innocent email: pri@dmu.ac.uk

There is some basic stuff on the web:

Fuzzy Logic

A Faq
A Tutorial
Another intro
A Mathematical Overview
Everything You've Always Wanted to know About Designing Fuzzy Logic Machines But Were Afraid to Ask
Fuzzy logic: an enabling technology
Another overview
When it comes to heavy Seattle traffic, 'fuzzy logic' smooths the flow
You fuzzyin' with me ?
Fuzzy Logic in Washing Machines

I found this on the web:

Several years ago, Steve Marsh, director of strategic operations at Motorola (Austin), visited Japan. Having checked into his hotel, he had only enough time to drop
his baggage in his room before hurrying to dinner with a customer. He entered the elevator and pushed the button of his floor. The elevator doors closed, and, in a
few seconds, opened again. Thinking that someone had pushed the elevator-request button, Steve held the door open and looked out, but no one was waiting.
When he reached again to push the button for his floor, he discovered he was already there. The elevator had transported him several floors without his feeling any
motion.

Being an engineer, Marsh was intrigued, so he commandeered the elevator and played awhile, moving between floors, both short and long distances, both up and
down. Concentrating now, he could feel the car moving but was amazed at the lack of its jerk in both starting and stopping and at its smooth acceleration and
deceleration. I don't know whether Steve made it to his dinner on time, but he later had Motorola personnel inquire about why the elevator's movement was so
smooth. The explanation was simple: Fuzzy logic controls the elevators. Steve became a believer.

Knowledge Based Systems

Fuzzy Logic

Search Engines

To help you search the web here are some search engines:

Maintained by Bob John.