Item for Distribution
B M Smith
bms at dcs.leeds.ac.uk
Wed Jan 16 11:06:40 EST 1991
PRELIMINARY CALL FOR PARTICIPATION
==================================
AISB91
University of Leeds
16-19 April 1991
Interested to know what is happening at the forefront of current AI
research?
Tired of going to AI conferences where you hear nothing but talk about
applications?
Bored at big AI conferences where there are so many parallel sessions
that you don't know where to go?
Saturated with small workshops that focus only on one narrow topic in
AI?
==> the 1991 AISB conference may be just the thing for you !
AISB91 is organized by the Society for the Study of Artificial
Intelligence and Simulation of Behaviour. It is not only the oldest
regular conference in Europe on AI - which spawned the ECAI
conferences in 1982 - but it is also the conference that has a
tradition of focusing on research as opposed to applications.
The 1991 edition of the conference is no different in this respect.
The conference has a single session and covers the full spectrum of AI
work, from robotics to knowledge systems. It is designed for
researchers active in AI who want to follow the complete field. Papers
were selected that are representative for ongoing research,
particularly for research topics that promise new exciting avenues
into a deeper understanding of intelligence.
There will be a tutorial programme on Tuesday 16 April, followed by
the technical programme from Wednesday 17 to Friday 19 April.
The conference will be held at Bodington Hall, University of Leeds, a
large student residence and conference centre. Bodington Hall is 4
miles from the centre of Leeds and set in 14 acres of private grounds.
Leeds/Bradford airport is 6 miles away, with frequent flights from
London Heathrow, Amsterdam and Paris. Leeds itself is easily
accessible by rail (2 and a half hours from London) and the motorway
network. The Yorkshire Dales National Park is close by, and the
historic city of York is only 30 minutes away by rail.
TECHNICAL PROGRAMME Wednesday 17 - Friday 19 April 1991
========================================================
The technical programme sessions are organized around problem areas,
not around approaches. This means sessions show how different schools
of AI - knowledge-based approaches, logic based approaches, and neural
networks - address the fundamental problems of AI.
The technical programme lasts 2 and a half days. Each day has a
morning session focusing on a particular area of AI. The first day
this area is distributed AI, the second day new modes of reasoning,
and the third day theorem proving and machine learning. The afternoon
is devoted to research topics which are at the forefront of current
research. On the first afternoon this topic is emergent functionality
and autonomous agents. It presents the new stream of ideas for
building autonomous agents featuring concepts like situatedness,
physical symbol grounding, reactive systems, and emergence. On the
second day the topic is knowledge level expert systems research. It
reflects the paradigm shift currently experienced in knowledge based
systems away from the symbol level and towards the knowledge level,
both for design and knowledge acquisition. Each session has first a
series of accepted papers, then two papers which treat the main theme
from a principled point of view, and finally a panel.
In addition the conference features three exciting invited speakers:
Andy Clark who talks about the philosophical foundations of AI, Rolf
Pfeifer who reflects on AI and emotion, and Tony Cohn who looks at the
formal modeling of common sense. The conference is closed by the
Programme Chairman, Luc Steels, who speculates on the role of
consciousness in Artificial Intelligence.
Here is a more detailed description of the various sessions and the
papers contained in them:
Distributed Intelligent Agents
==============================
Research in distributed AI is concerned with the problem of how
multiple agents and societies of agents can be organized to co-operate
and collectively solve a problem. The first paper by Chakravarty (MIT)
focuses on the problem of evolving agents in the context of Minsky's
society of mind theory. It addresses the question how new agents can
be formed by transforming existing ones and illustrates the theory
with an example from game playing. Smieja (GMD, Germany) focuses on
the problem of organizing networks of agents which consist internally
of neural networks. Smieja builds upon the seminal work of Selfridge
in the late fifties on the Pandemonium system. Bond (University of
California) addresses the problem of regulating co-operation between
agents. He seeks inspiration in sociological theory and proposes a
framework based on negotiation. Finally Mamede and Martins (Technical
University of Lisbon) address the problem of resource-bounded
reasoning within the context of logical inference.
Situatedness and emergence in autonomous agents
===============================================
Research on robots and autonomous agents used to be focused strongly
on low level mechanisms. As such there were few connections with the
core problems of AI. Recently, there has been a shift of emphasis
towards the construction of complete agents. This has lead to a review
of some traditional concepts, such as the hierarchical decomposition
of an agent into a perception module, a decision module and an action
module and it has returned robotics research to the front of the AI
stage. This session testifies to the renewed interest in the area.
It starts with a paper by Bersini (Free University of Brussels) which
is strongly within the new perspective of emphasizing situatedness and
non-symbolic relations between perception and action. It discusses the
trade-offs between reactive systems and goal-oriented systems. Seel
(STC Technology, Harlow, UK) provides some of the formal foundations
for understanding and building reactive systems. Jackson and Sharkey
(University of Exeter) address the problem of symbol grounding: how
signals can be related to concepts. They use a connectionist mechanism
to relate spatial descriptions with results from perception. Cliff
(University of Sussex) discusses an experiment in computational
neuroethology.
The next paper is from the Edinburgh Really Useful Robot project which
has built up a strong tradition in building autonomous mobile robots.
The paper will be given by Hallam (University of Edinburgh) and
discusses an experiment in real-time control using toy cars. The final
paper is by Kaelbling (Teleos Research, Palo Alto, California) who
elaborates her proposals for principled programming of autonomous
agents based on logical specifications.
The panel which ends the session tries to put the current work on
autonomous agents into the broader perspective of AI. The panel
includes Smithers (University of Edinburgh), Kaelbling, Connah
(Philips Research, UK), and Agre (University of Sussex).
Following this session, on Wednesday evening, the conference dinner
will be held at the National Museum of Photography, film and
Television at Bradford. The evening will include a special showing in
the IMAX auditorium, which has the largest cinema screen in Britain.
New modes of reasoning
======================
Reasoning remains one of the core topics of AI. This session explores
some of the current work to find new forms of reasoning. The first
paper by Hendler and Dickens (University of Maryland) looks at the
integration of neural networks and symbolic AI in the context of a
concrete example involving an underwater robot. Euzenat and Maesano
(CEDIAG/Bull, Louveciennes, France) address the problem of forgetting.
Pfahringer (University of Vienna) builds further on research in
constraint propagation in qualitative modelling. He proposes a
mechanism to improve efficiency through domain variables. Ghassem-Sani
and Steel (University of Essex) extend the arsenal of methods for
non-recursive planning by introducing a method derived from
mathematical induction.
The knowledge level perspective
===============================
Knowledge systems (also known as expert systems or knowledge-based
systems) continue to be the most successful area of AI application.
The conference does not focus on applications but on foundational
principles for building knowledge systems. Recently there has been an
important shift of emphasis from symbol level considerations (which
focus on the formalism in which a system is implemented) to knowledge
level considerations. The session highlights this shift in emphasis.
The first paper by Pierret-Golbreich and Delouis (Universite Paris
Sud) is related to work on the generic task architectures. It proposes
a framework including support tools for performing analysis of the
task structure of the knowledge system. Reichgelt and Shadbolt
(University of Nottingham) apply the knowledge level perspective to
the problem of knowledge acquisition. Wetter and Schmidt (IBM Germany)
focus on the formalization of the KADS interpretation models which is
one of the major frameworks for doing knowledge level design. Finally
Lackinger and Haselbock (University of Vienna) focus on domain models
in knowledge systems, particularly qualitative models for simulation
and control of dynamic systems.
Then there are two papers which directly address foundational issues.
The first one by Van de Velde (VUB AI Lab, Brussels) clarifies the
(difficult) concepts involved in knowledge level discussions of expert
systems, particularly the principle of rationality. Schreiber,
Akkermans and Wielinga (University of Amsterdam) critically examine
the suitability of the knowledge level for expert system design.
The panel involves Leitch (Heriot Watt University, Edinburgh),
Wielinga, Van de Velde, Sticklen (Michigan State University), and
Pfeifer (University of Zurich).
Theorem proving and Machine learning
=============== ================
The final set of papers focuses on recent work in theorem proving and
in machine learning. The first paper by Giunchiglia (IRST Trento,
Italy) and Walsh (University of Edinburgh) discusses how abstraction
can be used in theorem proving and presents solid evidence to show
that it is useful. Steel (University of Essex) proposes a new
inference scheme for modal logic.
Then there are two papers which represent current work on machine
learning. The first one by Churchill and Young (University of
Cambridge) reports on an experiment using SOAR concerned with
modelling representations of device knowledge. The second paper by
Elliott and Scott (University of Essex) compares instance-based and
generalization-based learning procedures.
TUTORIAL PROGRAMME - Tuesday 16 April 1991
==========================================
Six full-day tutorials will be offered on 16 April (subject to sufficient
registrations for each.)
Tutorial 1 Knowledge Base Coherence Checking
----------
Professor Jean-Pierre LAURENT
University of Savoie
FRANCE
Like conventional software, AI Systems also need validation tools.
Some of these tools must be specific, especially for validating
Knowledge-Based Systems, and in particular for checking the coherence
of a Knowledge Base (KB). In the introduction to this tutorial we
will clarify the distinctions to be made between Validation,
Verification, Static Analysis and Testing.
We will present methods which try to check exhaustively for the
coherence of a knowledge Base. Then we will present a pragmatic
approach in which, instead of trying to assert the global coherence of
a KB, it is proposed to check heuristically whether it contains
incoherences. This approach is illustrated by the SACCO System,
dealing with KBs which contain classes and objects, and furthermore
rules with variables.
Tutorial 2 Advanced Constraint Techniques
----------
Dr. Hans Werner Guesgen and Dr. Joachim Hertzberg
German National Centre for Computer Science (GMD)
Sankt Augustin,
GERMANY
This tutorial will present a coherent overview of the more recent
concepts and approaches to constraint reasoning. It presents the
concept of dynamic constraints as a formalism subsuming classical
constraint satisfaction, constraint manipulation and relaxation,
bearing a relationship to reflective systems; moreover, the tutorial
presents approaches to parallel implementations of constraint
satisfaction in general and dynamic constraints in particular.
Tutorial 3 Functional Representation and Modeling
----------
Prof. Jon Sticklen and Dr. Dean Allemang*
Michigan State University
USA
* Universitaet Zurich, SWITZERLAND
A growing body of AI research centres on using the known functions of
a device as indices to causal understanding of how the device "works".
The results of functional representation and modeling have typically
used this organization of causal understanding to produce tractable
solutions to inherently complex modelling problems.
In this tutorial, the fundamentals of functional representation and
reasoning will be explained. Liberal use of examples throughout will
illustrate the representational concepts underlying the functional
approach. Contacts with other model based reasoning (MBR) techniques
will be made whenever appropriate.
Sufficient background will be covered to make this suitable for both
those unacquainted with the MBR field, and for more experienced
individuals who may be working now in MBR research. A general
familiarity with AI is assumed.
Participants should send in with their registration materials a one
page description of a modeling problem which they face in their
domain.
Tutorial 4 Intelligent Pattern Recognition and Applications
----------
Prof. Patrick Wang M.I.T. Artificial Intelligence Laboratory and
Northeastern University, Boston USA
The core of pattern recognition, including "learning techniques" and
"inference" plays an important and central role in AI. On the other
hand, the methods in AI such as knowledge representation, semantic
networks, and heuristic searching algorithms can also be applied to
improve the pattern representation and matching techniques in many
pattern recognition problems - leading to "smart" pattern recognition.
Moreover, the recognition and understanding of sensory data like
speech or images, which are major concerns in pattern recognition,
have always been considered as important subfields of AI.
This tutorial includes overviews of pattern recognition and
articifical intelligence; including recent developments at MIT. The
focus of the tutorial will be on the overlap and interplay between
these fields.
Tutorial 5 SILICON SOULS - Philosophical foundations of computing and AI
----------
Prof. Aaron Sloman
University of Birmingham
This will not be a technical tutorial. Rather the tutor will introduce
a collection of philosophical questions about the nature of
computation, the aims of AI, connectionist and non-connectionist
approaches to AI, the relevance of computation to the study of mind,
varieties of mechanism, consciousness, and the nature of emotions and
other affective states. Considerable time will be provided for
discussion by participants.
Prof. Sloman has provided a list of pertinent questions, these will be
sent to participants upon registration.
Tutorial 6 Knowledge Acquisition
--------
Dr. Nigel Shadbolt
Nottingham University
Practical methods for acquiring knowledge from experts. The methods
described have been shown to be effective through the pioneering
research at Nottingham which compared common and less common methods
for eliciting knowledge from experts.
This tutorial is an updated version of the knowledge acquisition
tutorial given at AISB'89 which was well-attended and enthusiastically
received.
========================================================================
For further information on the tutorials, mail tutorials at hplb.hpl.hp.com or
tutorials at hplb.lb.hp.co.uk or tutorials%hplb.uucp at ukc.ac.uk
For a conference programme and registration form, or general information
about the conference, mail aisb91 at ai.leeds.ac.uk or write to:
Barbara Smith
AISB91 Local Organizer
School of Computer Studies
University of Leeds
Leeds LS2 9JT
U.K.
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