Meetings

Summary: T1: The role of Artificial Intelligence in Knowledge Management
T2: Language Technology: Applications and Techniques
T3: Introduction to Minimum Length Encoding Inference

Tutorials will be held 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.