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                 C A L L  F O R  P A R T I C I P A T I O N S

   AI'94 Tutorial on Fundamentals of Fuzzy Logic and Fuzzy Logic Controllers

                                    by

                      A. Sekercioglu and G. K. Egan 

                Laboratory for Concurrent Computing Systems 
                     Swinburne University of Technology       
                            {yas,gke}@swin.oz.au

                               22 November 1994


OVERVIEW

Fuzzy logic, along with artificial neural networks and genetic
algorithms constitutes a part of computational intelligence which, in
recent years, has become a focal point of research. It attempts to
model and mathematically encapsulate the approximate reasoning
processes under the influence of imprecise, incomplete and vague input
data. Fuzzy logic has strengths in modelling highly nonlinear,
complex sytems which are commonly  encountered in product design,
manufacturing and control.

One application of fuzzy logic, control systems utilizing some of its
concepts has become an important engineering tool. This tutorial
presents a comprehensive picture of fundamental issues of fuzzy logic
in order to study the structure and operation of fuzzy logic
controllers.


DETAILED PLAN

The tutorial will address these issues:

        * introduction and approaches to system modeling: comparison
	  of explicit, neural and fuzzy modeling methods,

	* crisp sets and fuzzy sets, basic concepts,

	* operations on fuzzy sets, t-norms and t-conorms,

	* fuzzy relations and composition of fuzzy relations,

	* fuzzy inference, generalized modus ponens, generalized modus 
	  tollens, fuzzy implication functions,

	* a brief look at intelligent control, 

	* fuzzy logic controllers: operation and examples.

Towards the end of the tutorial, a laboratory session is organized. In the 
laboratory session, participants will be able to test their ideas by writing 
their fuzzy rule bases for a simulated fuzzy logic controller in the  UNIX 
environment.


BIO-DATA OF THE PRESENTER

Ahmet Sekercioglu is a researcher at the Laboratory for Concurrent
Computing Systems, Swinburne University of Technology and lecturer at
the School of Electrical Engineering in the same university.  He has
obtained his B.Sc. and M.Sc. degrees from the Middle East Technical
University, Ankara, in 1982 and 1985 respectively. During the M.Sc.
studies he has worked as a research assistant at the Biomedical
Engineering laboratory of Middle East Technical University.  Later, he
has held research and development engineering positions at Aselsan and
Ericsson.  Currently he is working towards his Ph.D. degree in the
domain of Adaptive Fuzzy Systems.

Professor Gregory K. Egan is director of Laboratory for Concurrent
Computing Systems, Swinburne University of Technology and principal
lecturer at the School of Electrical Engineering. He has graduated in
Communication Engineering from RMIT in 1975. He has been awarded an
M.Sc. degree in computational methods in 1976, and, a Ph.D. in
parallel computer architectures in 1979 both at the Victoria
University of Manchester. From 1980 onwards he developed and led the
computer systems engineering groups and developed teaching programs at
RMIT and Swinburne University of Technology.


PREREQUISITES
 
This tutorial will present both introductory and in-depth material.
So, it will be suitable for both to novices, as well as to those with
some background who wish to extend their knowledge in the area of
fuzzy logic controllers. No prerequisite knowledge of the field is
necessary. However, some background in propositional logic and
classical set theory would be helpful.


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       Seventh Australian Joint Conference on Artificial Intelligence (AI'94) 

                       STRUCTURED SEQUENCE TUTORIALS

A structured sequence of pre-conference tutorials on several aspects of
applied AI has been organised. Tutorial participants will be able to select
from a choice of tutorials to suit their specialist requirements.

The following are the list of tutorials organised for AI'94. All
participants of any of these tutorials may attend the talk entitled
"Introduction to Artificial Intelligence". This is a complementary session
for all tutorial participants.
                    

Guide:
(y) - indicate YES
(n) - indicate NO
(t) - indicate THEORETICAL SESSION
(p) - indicate PRACTICAL SESSION
(d) - indicate DEMONSTRATION SESSION
1hr - indicate one hour
4s  - indicate four sessions
3s  - indicate three sessions 

Note: Each session is One and a Half hours long.


--------------------------------------------------------------------------------
No.     Tutorial Title               Presenters            CODE Length Practical
--------------------------------------------------------------------------------
[0] Introduction To Artificial       Not Confirmed yet     AI     1hr      -
    Intelligence

[1] Constraint-Based Reasoning       Dr. H. W. Guesgen     CBR    4s       y

[2] Fundamentals of Fuzzy Logic      Dr. A. Sekercioglu    FLFC   3s       y
    and Fuzzy Logic Controllers      G.K. Egan
               
[3] An Introduction to Evolutionary  Dr. X. Yao            EC     3s       y
    Computation                      Prof Z. Michalewicz
               
[4] Building Intelligent Decision    Dr. J. Zeleznikow     IDSS   3s       y
    Support Systems through the use  Mr. Dan Hunter
    of multiple reasonig Strategies
                
[5] Intelligent Learning Database    Dr Xindong Wu         ILDB   3s       y 
    Systems            

[6] Nonmonotonic Reasoning           Dr. M-A Williams      NMR    3s       y
                                     Dr. G. Antoniou

[7] Theoretical Foundations of       Dr. A. G. Hoffman     TFML   3s       n
    Machine Learning                 Dr. Shyam Kapur
                                     Dr. Arun Sharma

[8] Knowledge Acquisition and        Dr. P. Compton        KAM    3s       d
    Maintenance with Ripple Down 
    Rules     

[9] Hybrid (AI symbolic,             Dr. Nik K. Kasabov    HS     3s       d
    Connectionist, Fuzzy, Chaotic) 
    Systems


--------------------------------------------------------------------------------
Tentative Tutorial Timetable
--------------------------------------------------------------------------------

Monday 21/11/94 (Day 1)
=======================
 9.30 - 10.30:  Introduction to AI
10.30 - 11.00:  Morning Tea Break
11.00 - 12.30:  NMR(t)  IDSS(t) EC(t)   TFML(t)
12.30 -  2.00:  Lunch
 2.00 -  3.30:  NMR(t)  IDSS(t) EC(t)   TFML(t)
 3.30 -  4.00:  Afternoon Tea Break     
 4.00 -  5.30:  NMR(p)  IDDS(p) EC(p)   TFML(t)

Tuesday 22/11/94 (Day 2)
========================
 9.00 - 10.30:  CBR(t)  ILDB(t) FLFC(t) KAM(t)  HS(t)
10.30 - 11.00:  Morning Tea Break
11.00 - 12.30:  CBR(t)  ILDB(t) FLFC(t) KAM(t)  HS(t)
12.30 -  2.00:  Lunch
 2.00 -  3.30:  CBR(t)  ILDB(p) FLFC(p) KAM(d)  HS(d)   
 3.30 -  4.00:  Afternoon Tea Break
 4.00 -  5.30:  CBR(p)                               

--------------------------------------------------------------------------------
Examples of structured sequence of tutorials:
--------------------------------------------------------------------------------

There are couple of structured sequence of tutorials that one could adopt.
For example, if the participant is interested in logic/theoretical basis of
AI, then he/she may want to select the following sequence:

Day 1   9.30 - 10.30: Introdution to AI
       11.00 - 12.30: NMR(t)
        2.00 -  3.30: NMR(t)
        4.00 -  5.30: NMR(p)

Day 2   9.00 - 10.30: CBR(t)    
       11.00 - 12.30: CBR(t)
        2.00 -  3.30: CBR(t) 
        4.00 -  5.30: CBR(p)

Alternatively, if the participant is more interested in the applications of
AI, then the following sequence may be more suitable:

Day 1   9.30 - 10.30: Introdution to AI
       11.00 - 12.30: IDSS(t)
        2.00 -  3.30: IDSS(t)
        4.00 -  5.30: IDSS(p)

Day 2   9.00 - 10.30: FLFC(t)
       11.00 - 12.30: FLFC(t)
        2.00 -  3.30: FLFC(p) 


If the interest is in Machine Learning/Knowledge Acquisition, then the
possible sequence may be:

Day 1   9.30 - 10.30: Introdution to AI 
       11.00 - 12.30: TFML(t)
        2.00 -  3.30: TFML(t)
        4.00 -  5.30: TFML(t)

Day 2   9.00 - 10.30: ILDB(t)  or  KAM(t)    
       11.00 - 12.30: ILDB(t)  or  KAM(t)
        2.00 -  3.30: ILDB(p)  or  KAM(d)

Another possible sequence may be:

Day 1   9.30 - 10.30: Introdution to AI
       11.00 - 12.30: EC(t)
        2.00 -  3.30: EC(t)
        4.00 -  5.30: EC(p)

Day 2   9.00 - 10.30: HS(t)    
       11.00 - 12.30: HS(t)
        2.00 -  3.30: HS(d)
--------------------------------------------------------------------------------
Further Information:
--------------------------------------------------------------------------------
For further information on the structured sequence tutorials, please
contact the AI'94 Tutorial Co-ordinator, at the following address:

                        Dr. Dickson Lukose
         Department of Mathematics, Statistics, and Computing Science
                    University of New England
                        Armidale, N.S.W., 2351
                             AUSTRALIA

                 e-mail: ai94@fermat.une.edu.au
                 fax.  : (+61 67) 73 3312
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