DM: Tutorial Announcement at PRICAI2000 in Melbourne, Australia

Apologies, should you receive multiple copies of this mail.

             TUTORIALS  AT  PRICAI2000

on 28 & 29 August 2000, before the conference (30 August - 1 September 2000)



T1: The role of Artificial Intelligence in Knowledge Management by Eric Tsui
(28/8/2000 Morning)

Summary

Knowledge Management (KM) is an emerging field rapidly gaining
momentum both in the research arena and the business community.
This tutorial begins with a review of the dominant trends in the
development of Corporate Knowledge Management by linking recent
work to the background of researchers. A number of AI
areas/techniques are finding their way as strong enablers of KM.
Most noticeably, Intelligent Agents, Information Filtering and
Ontologies. Common KM applications will be described and these
include, among others, knowledge maps and corporate memories in
product development, software re-use, business processes and
knowledge communities. Challenges and predictions for KM will also
be outlined. This tutorial is especially suited to practitioners and
researchers who want to learn about the field of KM and the major
contributions of AI research and applications to this new field.

Biography of presenter

Eric Tsui joined the Expert Systems Group of Computer Sciences
Corporation (CSC) in 1989 after years of academic research in
automated knowledge acquisition, case-based reasoning and
knowledge engineering tools. He practices KM to CSC clients as well
as designs and delivers KM courses for two universities. He is also
the primary guest editor of the KBS Journal (Elsevier)'s forthcoming
Special Issue on AI in KM. His qualifications include B.Sc.(Hons.),
PhD, MBA and is an adjunct of the University of Sydney and
University of Technology, Sydney.



T2: Language Technology: Applications and Techniques by Associate
Professor

Robert Dale (28/8/2000 Afternoon)

Context and Motivation

Language Technology is the new millennium's practically-focussed rebirth of 
natural
language processing, covering applications from optical character 
recognition to
sophisticated spoken language dialog systems and intelligent search 
engines. Language
Technology is widely perceived by the IT industry to be a fundamental 
enabling technology
that will both enable smarter interfaces and provide assistance in 
overcoming the
information overload of the Internet age.

Tutorial Aims and Content

This tutorial aims to provide the attendee with a broad awareness of actual 
and potential
Language Technology applications, along with a framework for thinking about 
these
applications in terms of the linguistic resources they need. Attendees will 
acquire an
understanding of the scale of development required for different kinds of 
applications,
along with an appreciation of what constitutes a feasible application. 
Frequent reference
will be made to commercial applications, with corresponding critiques aimed 
at showing
how to assess claims made by vendors.

Biography of Presenter

Robert Dale has an international research reputation in natural language 
processing, and
particularly in natural language generation; he has presented numerous 
tutorials on these
topics at international conferences. He is author of over 50 journal and 
conference papers,
and is author and editor of a number of books in the area; most recently 
Building Natural
Language Generation Systems (Reiter and Dale 2000; Cambridge University 
Press) and the
forthcoming Handbook of Natural Language Processing: Tools and Techniques 
(Dale,
Moisl and Somers [eds], Dekker Publishing). He teaches part time at 
Macquarie University,
and is Director of Language Technology Pty Ltd, a consultancy focussing on 
cutting-edge
speech and language applications.



T3: Introduction to Minimum Length Encoding Inference by Dr. David Dowe
(29/8/2000 Morning)

Summary

The tutorial will be on Minimum Length Encoding, encompassing both Minimum 
Message
Length (MML) and Minimum Description Length (MDL) inductive inference. This 
work is
information-theoretic in nature, with a broad range of applications in 
machine learning,
statistics, "knowledge discovery" and "data mining". We discuss the 
following topics:
statistical parameter estimation;

        mixture modelling (or clustering) of continuous, discrete and 
circular data;
        clustering with correlated attributes;
        learning decision trees;
        learning decision trees with Markov model leaf regressions;
        learning probabilistic finite state machines;

and possibly other problems if time permits. We will also show the 
successes of MML
compared to other methods both in fitting polynomial functions and in 
modelling and
fitting an alternating binary process. MML is statistically consistent and 
efficient,
meaning that it converges as quickly as is possible to any true underlying 
data-generating
process. It is also invariant under 1-to-1 re-parameterisation of the 
problem and has a
better than good track record in problems of machine learning, statistics 
and ``data
mining''. Some of the above machine learning techniques will then be applied to
real-world problems, such as protein structure prediction and the human 
genome project,
lossless image compression, exploration geology, business forecasting, 
market inefficiency
and natural language. Passing mention will be made of foundational issues 
such as
connections to Kolmogorov-Solomonoff-Chaitin complexity (see recent 
special  issue of
the Computer Journal), universal modelling and (probabilistic) prediction.

  Biography of presenter

Dr David Dowe works primarily with Lloyd Allison, Trevor Dix, Chris Wallace and
others in the Minimum Message Length (MML) group at the School of Computer 
Science
and Software Engineering at Monash University. Most of his work for the 
past 9 years has
been in the theory and applications of the (information-theoretic) MML 
principle of
statistical and inductive inference and machine learning (and "knowledge 
discovery" and
"data mining"), a principle which dates back to Wallace and Boulton (Comp. 
J., 1968), and
which has been surveyed more recently in Wallace and Freeman (J. Roy. Stat. 
Soc., 1987)
and Wallace and Dowe (Comp. J., 1999).

David was Program Chair of the Information, Statistics and Induction in 
Science (ISIS)
conference, held in Melbourne, Australia on 20-23 August 1996; attended by 
R. J.
Solomonoff, C. S. Wallace, J. J. Rissanen, J. R. Quinlan, Marvin Minsky, 
and others.



  T4: Case-Based Reasoning in the Finance and Service Sectors by Ian Watson
(29/8/2000 Morning)

Summary

Case-based reasoning (CBR) has long been successfully used in customer support
applications, in particular in help-desks for the technical support of 
products and services
via the Internet. Therefore, it is a natural extension for CBR to support 
the selection,
customisation and sale of products and services in e-commerce systems in 
what is being
termed customer relationship management (CRM). This tutorial will introduce 
attendees
to the concepts underpinning CBR and illustrate why its concepts of 
similarity, reuse,
adaptation and retention are so appropriate to CRM. A framework for the 
delivery of
intelligent services for e-commerce systems based on CBR, XML and Java will be
illustrated with fielded systems operating in many application areas, 
including: finance,
real estate, travel agencies and used car sales.

Biography of presenter

Ian Watson is a Senior Lecturer in the Department of Computer Science at 
the University of
Auckland in New Zealand. Ian was the founder of AI-CBR (www.ai-cbr.org) the 
leading
Internet site for CBR researchers and developers and is the author of "Applying
Case-Based Reasoning: techniques for enterprise systems" the first book on the
application of CBR. Ian was awarded a "Distinguished Paper" award at 
IJCAI-99 for work
on a distributed CBR system for engineering sales support and will co-chair 
the 4th.
International Conference on Case-Based Reasoning (ICCBR'01) in July 2001 in 
Vancouver.





T5: Designing Human-Centered Autonomous Agents by Gregory Dorais and David
Kortenkamp (29/8/2000 Afternoon)

Summary

This tutorial will present requirements and architectural guidelines for 
designing
autonomous systems that include humans and autonomous agents who interact 
to achieve
complex goals. We call such systems human-centered autonomous agents. This 
tutorial
draws relevant research from each of these areas. We will particularly 
focus on identifying
guidelines for autonomous agents that will enable users, other software 
agents, or the
agent itself to dynamically change the "level of autonomy" within a 
spectrum ranging from
complete human control to complete autonomous control. We refer to this 
capability as
adjustable autonomy and it is a key feature of human-centered autonomous 
agents. In this
tutorial we will present the state-of-the-art in human-centered autonomous 
agents and
give guidelines and a methodology for developing such agents. These will be 
supported by a
running example. We will finish by describing some applications of 
human-centered
autonomous agents. Our goal is to provide insight to agent designers on how 
to create
autonomous systems that minimize the necessity for human interaction, but 
maximize the
capability for humans to interact at whatever level of control is most 
appropriate.

Biography of presenters

Dr. Gregory Dorais is a computer scientist in the Autonomy and Robotics 
Area in the
Computational Sciences Division of the NASA Ames Research Center. He 
received both his
Ph.D. and M.S. in Computer Science from the University of Michigan and his 
B.S. in
Management Information Systems from Oakland University. He is a co-principal
investigator of the "Intelligent Deployable Execution Agent" project at 
NASA. Dr. Dorais
was the integration lead of the Remote Agent experiment for the Deep Space 
1 spacecraft
which was the first AI agent-controlled spacecraft featuring an on-board 
planner and a
model-based inference system. He has performed autonomous rover research at 
JPL and
remote sensing research at General Motors Research.

Dr. Dorais co-organized the 1999 AAAI Spring Symposium on "Agents with 
Adjustable
Autonomy" and the 1999 IJCAI workshop on "Adjustably Autonomous Systems". 
He was
on the program committee of the 1999 Autonomous Agents workshop on "Autonomy
Control Software".

David Kortenkamp is a senior scientist with Metrica Inc./TRACLabs 
supporting NASA
Johnson Space Center. He has a PhD and MS in computer science and 
engineering from the
University of Michigan and a BS in computer science from the University of 
Minnesota. At
NASA, Dr. Kortenkamp is co-principal investigator (with Dr. Gregory Dorais) 
of  the
"Human-Centered Autonomous Agents" project. He has also co-organized a AAAI
Spring Symposium on "Agents with Adjustable Autonomy" and an IJCAI workshop on
"Adjustable Autonomy Systems". He is guest editor with Henry Hexmoor of an 
upcoming
JETAI special issue on Autonomous Control Systems and is associate editor 
of the MIT
Press series on Intelligent Robotics and Autonomous Agents. Dr. Kortenkamp 
has given
numerous invited talks on the subject of human-centered autonomous agents 
including a
Robotics Institute Seminar at Carnegie Mellon University and an Artificial 
Intelligence
Seminar at the University of Virginia. He is on the program committee of 
Autonomous
Agents 2000 and AAAI-2000.

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