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    Seventh Australian Joint Conference on Artificial Intelligence (AI'94)
                    "Sowing the Seeds for the Future"

                 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 Intelligent Learning Database Systems            

                                     by 

                                Dr Xindong Wu
	       Dept. of Computer Science, James Cook University,
                    Townsville, Queensland 4811, Australia
			xindong@coral.cs.jcu.edu.au

                               22 November 1994


ABSTRACT

Knowledge acquisition from databases is a research frontier for both database
technology and machine learning (ML) techniques, and has seen sustained
research over recent years.  It also acts as a link between the two fields,
thus offering a dual benefit.  Firstly, since database technology has already
found wide application in many fields, ML research obviously stands to gain
from this greater exposure and established technological foundation. Secondly,
ML techniques can augment the ability of existing database systems to
represent, acquire, and process a collection of expertise such as those which
form part of the semantics of many advanced applications (e.g. CAD/CAM). This
full-day tutorial will present and discuss techniques for the following 3
interconnected phases in constructing intelligent learning database systems:
(1) Translation of standard database information into a form suitable for use
by a rule-based system; (2) Using machine learning techniques to produce rule
bases from databases; and (3) Interpreting the rules produced to solve users'
problems and/or reduce data spaces.  It will suit a wide audience (including
postgraduate students and industrial people) from databases, expert systems,
and machine learning.


CONTENTS

	1. Knowledge Acquisition from Databases: Problem and Domain
		1.1 Problems in Conventional Databases
		1.2 Research Topics in Intelligent Databases
		1.3 Requirements for Knowledge Discovery in Databases
	2. Typical Inductive Learning Algorithms
		2.1 The ID3 Family
		2.2 The AQ Family
		2.3 The HCV Family
	3. Integrating More Semantic Information into Data Models
		3.1 The E-R Model
		3.2 Deductive and Object-Oriented Databases
		3.3 More Expressive Representations
	4. An Intelligent Learning Database System
		To introduce a PC shell developed at Edinburgh
	5. Conclusions and Research Directions in the Field
	6. A Practical Component Using a PC Lab


COURSE MATERIAL

 - Xindong Wu, Research Issues in Intelligent Learning Database Systems,
   Proceedings of the Seventh Annual Florida AI Research Symposium, Pensacola
   Beach, Florida, U.S.A., May 5-7, 1994, 137--141.
 - Xindong Wu, Inductive Learning: Algorithms and Frontiers, Artificial
   Intelligence Review, 7(1993), 2: 93-108.
 - Xindong Wu, KEshell2: An Intelligent Learning Data Base System, Research and
   Development in Expert Systems IX, M.A. Bramer and R.W. Milne (Eds.),
   Cambridge University Press, U.K., 1992, 253--272.


BIO-DATA OF THE PRESENTER

Dr Xindong Wu received his first and Master's degrees in Computer Science from
Hefei University of Technology, China, and his Ph.D. in Artificial Intelligence
from the University of Edinburgh, Britain. In the past, he has authored 2
technical books, Expert Systems Technology (1988) and Constructing Expert
Systems (1990). He has also published over 60 papers in various periodicals
(such as Expert Systems: The International Journal of Knowledge Engineering,
Artificial Intelligence Review, Informatica, and the Journal of Computer
Science and Technology) and in conference proceedings (e.g., Research and
Development in Expert Systems IX, and the 21st ACM Computer Science
Conference).  His technical interests include machine learning, expert systems,
intelligent database systems, and knowledge-based software engineering. He is
an editor on the Editorial Board of the Europe-based Informatica: An
International Journal of Computing and Informatics, and a member of the
Editorial Board of the U.S.A.-based International Journal of Computers and
Their Applications.  He has taught courses in Combinatorial Mathematics,
Expert Systems, Knowledge Representation and Inference, Machine Learning,
Advanced Data Structures and Databases, Introduction to Computer Science, and
Artificial Intelligence.


PREREQUISITES

Databases, Expert Systems, and (preferably) Prolog.


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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


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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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