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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 Theoretical Foundations of Machine Learning
		(Full-day Tutorial on 21 Nov 94)
                           by

                      Dr Achim Hoffmann
         School of Computer Science and Engineering
              The University of New South Wales
              Sydney, NSW, 2052, Australia
                   achim@cse.unsw.edu.au

                      Dr Shyam Kapur
         Dept. of Computer Science, James Cook University,
             Townsville, Queensland 4811, Australia
                  kapur@coral.cs.jcu.edu.au

                       Dr Arun Sharma
         School of Computer Science and Engineering
               The University of New South Wales
                  Sydney, NSW, 2052, Australia
                     arun@cse.unsw.edu.au

ABSTRACT

The act of learning is one of the most important hallmarks of what 
it means to  be intelligent  and  most AI  systems  are  far from  
exhibiting this property. Realization of this deficiency in expert 
systems  has  caused learning  to become  a central  issue  in AI  
today.   Herbert Simon, in his  keynote  address  at last year's
AAAI, noted that Machine Learning will increasingly  take  center
stage  in  AI throughout the next decade.  Most future AI systems
will need some ability to learn and to adapt to changes in  their
environments unforeseen by the system developers.

Machine Learning certainly is one of the hardest problems  in  AI
today.  Despite considerable progress, it can be argued that some
approaches to Machine Learning are  still  practiced  as  ``black
art.''   Sometimes  a  learning  algorithm  achieves  astonishing
results; and yet at other times they perform so poorly  that  the
user  ends  up  tinkering  with the system without any reasonable
return for their effort.

Fundamental investigations into the problems  and  challenges  of
Machine  Learning have been conducted in Philosophy for ages, and
more recently such studies have also been conducted  in  Computer
Science.

GOALS OF THE TUTORIAL

1. To clearly  lay down the central conceptual ingredients  of  a
learning   framework.    These   ingredients   are   not  clearly
distinguished in many practical approaches.  

2. An introduction to the mathematical analysis of learning  tasks  
and  the  basic techniques  of  Computational  Learning  Theory.   

3. The  interplay between  learning  algorithm,  training  data, 
computing time and success criteria will be explored.

DETAILED PLAN 
1. Computational Limitations on Learning         
   1.1 Learnability from  positive  data.          
   1.2 Learnability from positive and negative data.          
   1.3 Batch and Incremental  Learning.   
2. Heuristics   for   Learning           
   2.1 Monotonic  Heuristics.
   2.2 Consistent and  Inconsistent  Heuristics          
   2.3 Pattern   Languages.    
3. Resource  Requirements  for  Learning
   3.1 Introduction to probably approximately correct learning          
   3.2 Bounds on required number  of  training examples.           
   3.3 Computational  Complexity of Learning Tasks.          
   3.4 Analysis of Neural Network's Learning Power.

COURSE MATERIAL:

Lecture notes and bibliographical references will be provided for
every participant.

BIO-DATA OF THE PRESENTERS

Dr. Dr. ACHIM G. HOFFMANN received his PhD in Computer Science in
1992   from  Technische  Universitaet  Berlin  (West).   1993  he
received his  PhD in Philosophy from the same institution.  Since
January  1993  he  is  Lecturer  at  UNSW.   His current research
interests   include   Machine    Learning,    Neural    Networks,
Computational Learning Theory and the Philosophical Foundations of
AI.  He has published some 20 papers in Journals (e.g. Journal of
Experimental    and   Theoretical   AI,   Neurocomputing   -   An
International Journal) and Conference  Proceedings  (e.g.  IJCAI,
ECAI, IJCNN) in the areas of Design Automation, Machine Learning,
and  the Philosophical Foundations of AI.

Dr. SHYAM KAPUR received his Ph.D. also in Computer Science  from
Cornell  University.   He  was a Post Doctoral Research Fellow at
the Institute for Research in Cognitive  Science  (University  of
Pennsylvania)  from  1991  to  1993.   He  has published about 20
papers in journals (such  as  Theoretical  Computer  Science  and
Cognition), in book volumes, and in conference proceedings (COLT,
ALT). His technical interests include computational learning theory
and  computational  linguistics especially as they pertain to the
cognitive modeling  of  both  natural  language  acquisition  and
processing.   He  has  taught  introductory  classes  in computer
science and an honours-level class in artificial intelligence.

Dr. ARUN SHARMA received his PhD in  Computer  Science  from  the
State  University  of  New York at Buffalo.  Prior to joining the
School of Computer Science and Engineering  at  UNSW,  he  was  a
post-doctoral  associate in the Department of Brain and Cognitive
Sciences at the MIT.   He has published over twenty five articles
in   journals  (e.g., Information  and  Computation,  Journal  of
Computer and System Sciences, and Theoretical  Computer  Science)
and  conferences  (e.g., COLT,  ALT,  and  ICALP).  His technical
interests  include  Learning  Theory,   Machine   Learning,   and
Computability and Complexity.


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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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