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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 Building Intelligent Decision Support Systems through the 
                      use of multiple reasoning strategies

				     by

  Dr John Zeleznikow                 and        Mr. Dan Hunter
  Database Research Laboratory                  Law School
  Applied Computing Research Institute          University of Melbourne
  La Trobe University                           Parkville, Victoria
  Australia 3083                                Australia 3052

  johnz@latcs1.lat.oz.au                        dah@rumpole.law.unimelb.edu.au

                                21 November 1994


ABSTRACT

This tutorial will demonstrate how attendees can move beyond using rule 
based reasoning to build intelligent decision support systems.  In this 
tutorial we focus upon supplementing deductive reasoning with case based 
reasoning, neural networks and intelligent information retrieval.  Applications 
of these principles in law, management and finance will be provided.

The tutorial will provide background knowledge necessary to understand the 
lectures of Professor Sowa and Sycara.


OUTLINE OF THE TUTORIAL

*	Introduction 
	- The need for intelligent decision support systems in finance, 
          management and law

*	Strategies for providing Intelligent Decision Support Systems 
        - Intelligent Information Retrieval, Deductive Reasoning, Case Based 
          Reasoning

* 	Intelligent Information Retrieval 
        - Keyword retrieval, Boolean retrieval, Vector space models,
          Probabilistic models.

*	Introduction to deductive reasoning systems 
	- Production rule systems, Logic programming approaches,
          Object oriented approaches.

*	Case based reasoning systems 
	- What are based reasoners? Why use case based reasoners? 
	  Constructing differing types of case based reasoners. Examples of 
          case based reasoners: MEDIATOR, PERSUADER, CASEY, HYPO, PROTOS 
          Automated design of case bases.

*	Integrating deductive and case based reasoning 
	- Using the object oriented approach to build integrated systems. 
	  The relative benefits and disadvantages of blackboard systems and 
	  distributed artificial intelligence to build integrated systems. 
	  Examples of integrated systems: ANAPRON, CABARET, GREBE, IKBALS, 
          PROLEXS

*	Combining information retrieval, deductive reasoning, neural networks 
	and case based reasoning 
	- Conceptual information retrieval and neural networks, Bayesian 
          Inference Networks, hypertext, The DATALEX workstation, AIR and 
          SCALIR, WIN (Westlaw Is Natural), SPLIT UP.

*	Conclusion


ASSOCIATED PRACTICAL WORK

There will be two practical components in the tutorial

     Participants will have the opportunity to examine and use integrated
     systems built by the tutorial presenters: in particular IKBALS and
     FLORENCE -- integrated rule based/case based reasoners -- and SPLIT-UP
     an integrated rule based/neural network system.

     Participants will build legal knowledge based systems under the 
     guidance of the tutorial presenters. They will have the opportunity to
     examine how to supplement rule based reasoning with one or more of the 
     reasoning and retrieval approaches discussed in the tutorial. 


BIO-DATA OF THE TUTORIAL PRESENTERS

Dr John Zeleznikow B.Sc,(Hons), PhD., GradDipComp is the Head of the Database 
Research Laboratory, Applied Computing Research Institute, and a Senior 
Lecturer in the Department of Computer Science and Computer Engineering, La 
Trobe University, Melbourne.  His area of research involves using 
multiple reasoning strategies to build intelligent decision support systems.  
Dr Zeleznikow is the chairman of the Database Technical Committee of the 
Australian Computer Society and was an associate editor of the Australian 
Computer Journal.  He was the General Chairman of DS-5 an IFIP Conference on 
the Semantics of Interoperable Databases, and was tutorial chairman of the 
International Conference on Software Engineering and the Very Large Databases 
Conference.  He will be General Chairman of the Sixth International Conference 
on Artificial Intelligence and Law (an ACM conference).  He has written over 50 
refereed papers and edited five books.

Daniel Hunter BSc LL.B (Hons) is a lecturer at the School of Law, University of 
Melbourne.  He is joint editor of Computers and Law, the publication of the 
Austrlaian and New Zealand Societies for Computers and Law.  He is a Barrister 
and Solicitor of the Supreme Court of Victoria and High Court of Australia, and 
has worked as a computer programmer and in practice as a solicitor.

Together, Dr Zeleznikow and Mr Hunter have written a number of articles on 
artificial intelligence and law.  They have recently published a book, in the 
Kluwer Law and Taxation Series, `Building Intelligent Legal Information Systems 
- Representation and Reasoning in Law'.  Together they are to edit a special 
edition of the journal Applied Expert Systems devoted to the topic Legal Expert 
Systems, and gave a tutorial at the Fourth National Conference on Law, 
Computers and Artificial Intelligence (Exeter, United Kingdom).


PREREQUISITES

A basic knowledge of deductive reasoning and rule based expert systems.



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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)
--------------------------------------------------------------------------------
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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