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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 Nonmonotonic Reasoning

                                    by

                   Grigoris Antoniou and Mary-Anne Williams
                          Information Systems Group
                             Dept of Management
                        University of Newcastle, 2308

		    ga@tyr.informatik.Uni-Osnabrueck.de 
                       maryanne@frey.newcastle.edu.au

			       21 November 1994


ABSTRACT

Complete information is difficult to come by and generally not available
even in simple database applications. Consequently, an intelligent
reasoning system must be capable of making plausible conjectures, which
may be retracted when found to be incorrect according to new information
that becomes available. Nonmonotonic Reasoning provides a mechanism for an
intelligent reasoning system to make such conjectures when its knowledge
is incomplete;  this is accomplished by sanctioning inferences by default
or by determining those that are most likely according to some criteria. 

The tutorial will consist of a theoretical and a practical component.  In
the theoretical component we aim to provide a conceptual framework for
Nonmonotonic Reasoning. Rather than discussing a very broad set of topics,
we will focus on one of the main approaches to Nonmonotonic Reasoning,
namely default logic.  In particular, we will focus on how one can
represent problems and how one may determine extensions within the default
logic formalism.  In practice, determining extensions is one of the main
obstacles when implementing and applying default logic, and is very often
neglected in tutorials, courses, and even books.  The practical component
comprises the hands-on development of an example whose purpose is to
illustrate the utility of Nonmonotonic Reasoning. 

Participants will obtain an appreciation of some methods in the field of
Nonmonotonic Reasoning, and will also explore tools and techniques which
are very useful in practice. 


TUTORIAL OUTLINE

The topics to be covered are:
 
1. Nonmonotonic Reasoning: What is it all about?

2. Types of Nonmonotonic Reasoning

        2.1 Nonmonotonic inference relations
	2.2 Closed world assumption
        2.3 Predicate Completion
        2.4 Circumscription
        2.5 Default Logic
        2.6 Autoepistemic Logic

3. Default Logic

	3.1 Default rules and theories
	3.2 Extensions
	3.3 Computing extensions
	3.4 Normal default theories

4. Discussion of a prototypical Prolog implementation.

5. Practical session in PC Lab.

6. Summary


PREREQUISTES

Predicate Calculus, and elementary Prolog.


SCHOLARSHIPS

(a) Two scholarships will be granted for the full cost of the tutorial. To
    be considered for these scholarships submit the following information
    to Dr Mary-Anne Williams by October 7, 1994: 

		(i) Name, contact address, email (if available),
	       (ii) Academic Record,
              (iii) Statement of research interests,
               (iv) Recommendations from two academics acquainted with
		    the candidates record.

(b) Criteria to be satisfied by the applicants:
	Must have, or anticipate receiving a good Honours Degree by the
      	end of 1994.

(c) Procedure for selection of the candidates that will receive the
    scholarships:
	Successful candidates will be notified of their success or otherwise
        by October 14, 1994.



BIO-DATA for the Presenters

Grigoris Antoniou studied computer science at the University of Karlsruhe
in Germany and earned his Doctorate at the University of Osnabrueck,
Germany. He has been working in the Department of Mathematics and Computer
Science in Osnabrueck since 1987, firstly as Research Assistant and now as
Lecturer. Beginning summer 1994, he will be a Lecturer in Information
Systems at the University of Newcastle. His research interests are the
logical foundations of computer science and AI. He is a co-author of the
book ``Logic: A Foundation of Computer Science'', Addison-Wesley 1991. He
has been working on Nonmonotonic Reasoning for the last few years and has
several published and forthcoming articles in the area. In the winter
semester 1992/93 he gave a course on Nonmonotonic Logic at the University
of Osnabrueck. In 1993 he gave a five-hours tutorial at the German Spring
School on Artificial Intelligence (KIFS-93). 

Mary-Anne Williams completed a Bachelor of Science, Diploma in Computer
Science and Master of Science at the University of New England, and has
recently completed her Doctorate at the University of Sydney. She is a
member of the Knowledge Systems Group, a research group in Computer
Science at the University of Sydney, and currently works as a Lecturer in
the Information Systems Group at the University of Newcastle. Her research
interests lie in knowledge representation, nonmonotonic reasoning, belief
revision, reasoning about action and explanation, in which she has
numerous published articles. She was one of the organisers of the highly
successful Workshop on Belief Revision at AI'93 in Melbourne last year. 


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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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