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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 Hybrid (AI symbolic, Connectionist, Fuzzy, Chaotic) Systems

                                    by 

                              Dr Nik K Kasabov, 
           Department of Information Science, University of Otago
                      P.O.Box 56, Dunedin, New Zealand 
              Fax: + 64 3 479 8311, Phone: + 64 3 479 8319
                         email: nkasabov@otago.ac.nz

                               with  Dr. LC Jain,
   Knowledge Engineering Systems Group, University of South Australia
                         The Levels,SA 5095, Australia
                    Phone: (08) 302 3315, Fax: (08) 302 3384 
                        email: etlcj@levels.unisa.edu.au

  
  
ABSTRACT

The current development of knowledge engineering brings to use very powerful 
methods for representing and dealing with raw data, incomplete or imprecise 
data and knowledge, uncertainty, common sense knowledge, complex systems. And 
these are the connectionist methods, the methods of fuzzy logic and the chaos 
theory, which complement the existing AI symbolic methods. What are the main 
principles of the symbolic AI methods, the connectionist methods, the methods 
of fuzzy systems and chaos theory and how can one use if necessary, all of them 
when creating an information system? This is the main theme of the tutorial. 

First, the main principles of using symbolic, fuzzy and neuro systems for 
problem solving will be discussed and compared. Then hybrid systems will be 
introduced. A hybrid symbolic-fuzzy-neural environment will be used for 
demonstration and practical examples will be given as illustrations. Different 
techniques for solving difficult problems in a hybrid environment will be 
demonstrated.  Connectionist and fuzzy systems can compliment each other very 
well. How to use neural networks for learning fuzzy rules and how to implement 
fuzzy rules in a connectionist structure will also be discussed. A picture on 
the current development of fuzzy neurons and fuzzy neural networks will be 
given. 

Another topic presented in the tutorial is chaos theory. Chaos theory has 
tremendous potential in many areas. It is not only about the infinite variety 
of `patterns' which can be generated by using a chaotic function with a lot of 
practical applications, but it is also the way we can simulate or approximate 
complex real systems. Chaotic neurons and chaotic neural networks will be 
presented with some of their applications. 

Having different techniques implemented in one hybrid environment facilitates 
creating more powerful information processing systems than any of its 
components. So, the main advantage of using hybrid systems is that they have 
all the advantages of their subsystems which make them very powerful tools 
especially for solving AI problems. 

   
OUTLINE OF THE TUTORIAL

1. An Introduction to the principles of symbolic AI systems, connectionist 
   systems and fuzzy systems 
   1.1. The symbolic AI methods and the BIG picture of knowledge engineering.
   1.2. Neural networks for problem solving: principles; design; applications; 
        practical examples.
   1.3. Fuzzy rule-based systems: principles; design; applications; practical 
        examples. 
 
2. Hybrid symbolic-neuro-fuzzy systems
   2.1. How to combine symbolic AI, connectionist and fuzzy systems?
   2.2. Hybrid system tools. Fuzzy COPE - a hybrid tool based on CLIPS, neural 
        networks and fuzzy systems. 
   2.3. Fuzzy rules extraction from neural networks.
   2.4. Applications of hybrid systems. Practical examples.   

3. Fuzzy neurons and fuzzy neural networks
   3.1. Fuzzy-, and neo fuzzy neurons 
   3.2. Fuzzy neural networks and their applications 
 
4. Chaotic systems 
   4.1. What is chaos?
   4.2. Chaotic neurons and chaotic neural networks

5. Further development of the hybrid systems 


DURATION:  3 academic hours

PREREQUISITE KNOWLEDGE:  The basics of AI and the principles of expert systems. 


BIO-DATA OF THE PRESENTERS

Dr. Nikola Kasabov is a Senior Lecturer in the Department of Information 
Science with the University of Otago. Previously he has been an Associate 
Professor at the Technical University in Sofia and a visiting lecturer at the 
University of Essex, UK. He has published over 85 papers, 5 books and 5 patents 
in the area of connectionist and hybrid connectionist systems, fuzzy systems, 
parallel programming. He is member of IEEE, INNS, NZCS, ENNS, BNNS, PARS and 
other international organisations. He is currently the Chairman of the ANNES 
(Artificial Neural Networks and Expert Systems) Special Interest Group in New 
Zealand which is part of the New Zealand Computer Society; and also - Chairman 
of the First ANNES'93 international conference; liaison chairman of the 
ANZIIS'94; member of the program and advisory board of ICONIP'94 and other 
international conferences on intelligent information systems.

Dr L.C. Jain is a Leader of the Knowledge-based Engineering Systems Group(KES),
one of the four groups located in the School of Electronic Engineering,
University of South Australia.  In addition to books, he has published a hosts 
of invited papers and presented workshops. His interests focus on System design,
diagnosis and signal processing using expert systems, neural networks, fuzzy 
logic, genetic algorithms and chaos theory.
     

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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
               
[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


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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)
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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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