(WITH APOLOGIES FOR MULTIPLE POSTINGS)
We write to ask for non-binding expressions of interest in a possible AAAI Fall
Symposium on Chance Discovery, proposed to be held in the northern
hemispheric Fall of 2002 on the east coast of the USA.
Chance events are rare or novel events with potentially significant consequences
for decision-making in some domain. It is planned that this symposium will be
devoted to two primary questions: How may we predict, identify or explain
chance events and their consequences? ("Chance Discovery") and How may we
assess, prepare for or manage them? ("Chance Management"). Further
information on the subject of the proposed symposium is attached below this
email. In addition, a review of a recent workshop in Japan on this topic can
be found at:
http://www.csc.liv.ac.uk/~peter/cd2001.html
The American Association for Artificial Intelligence (AAAI) conducts two sets
of symposia, in the Spring and Fall of each year, with the aim of discussing
emerging subjects which do not yet have their own regular conferences. These
meetings generally consist of 25-70 people, and encourage inter-disciplinary
themes and participation, both within AI and with other disciplines. Further
information about the AAAI Symposia can be found on the Association's pages
at:
http://www.aaai.org/Symposia/symposia.html
We seek expressions of interest from anyone potentially interested in attending
or contributing to the proposed symposium. These expressions are for planning
purposes only, and will not be taken as firm commitments to attend or
to participate. In due course, if the proposed symposium is approved by the
AAAI, a Call for Participation will be broadcast.
Please send expressions of interest by email to Peter McBurney at:
With thanks,
Yukio Ohsawa, Simon Parsons and Peter McBurney
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Peter McBurney
Agent Applications, Research and Technology (Agent ART) Group
Department of Computer Science
University of Liverpool
Liverpool L69 7ZF
U.K.
Tel: + 44 151 794 6768
Email: P.J.McBurney@csc.liv.ac.uk
Web page: www.csc.liv.ac.uk/~peter/
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PROPOSED AAAI FALL 2002 SYMPOSIUM:
A. TITLE: Chance Discovery: The Discovery and Management of Chance Events
B. DESCRIPTION:
Chance events are rare or novel events with potentially significant consequences
for decision-making in some domain. This symposium will be devoted to two
questions: How may we predict, identify or explain chance events and their
consequences? ("Chance Discovery") and How may we assess, prepare for or
manage them? ("Chance Management").
A robot or agent engaged in planning needs to adopt a view of the future: In
order to decide its goals, and then to decide which is the best sequence of
actions to achieve these goals, the agent must make some assumptions --
explicitly or implicitly -- regarding the evolution (or constancy) of its
environment and the impact of its planned actions on that environment. Any
rational action requires the prior adoption of a forecast; moreover, because
the environment may respond in unexpected ways to the actions of the
agent, the agent's forecast also needs continual revision in the light of
events.
But how can an agent (or a group of agents jointly) discover rare or
novel events and forecast their consequences? Their very unlikeliness makes
them difficult to predict or explain by methods which use historical data or
pattern-matching -- the methods of time series analysis, data mining or
association rule detection. One can think of this prediction or explanation
problem as a search for a global maximum (or minimum) of a surface whose shape
and features are unknown, in a space whose dimensions may also be unknown.
What search algorithms are known to work well in conditions of such ignorance?
And once a rare event has been identified, what are its consequences for the
agents concerned? A planning agent cannot usually ignore these events, as
their consequences may significantly impede or facilitate the achievement of
its goals. For example, although strong earthquakes occur in major urban
centres only rarely (relative to all the earth tremors that occur around the
world), such earthquakes tend to have human and economic consequences well
beyond that of the typical tremor. A rational public safety body for a city in
an earthquke-prone area would plan for such contingencies even though the
chance of a strong quake is still very small.
In talking of chance events, the word "chance" is used, rather than (say)
"risk" because such events are not necessarily bad in their consequences.
Forecasting market demand for innovative products is an example. We can ask
potential customers what they may think of a new product, but if they have no
experience of it, or of anything similar, their responses may not be
meaningful. Nor may past demand for similar or substitute products provide us
with much guidance: it makes no sense, for instance, to forecast the demand
for a new bridge by counting the numbers of people who currently swim across
the river it will span. And what appears as a positive chance event to some
- -- bridge builders, for example -- may be catastrophic to others -- e.g.
operators of river boats. Thus, the perception and management of chance events
may be crucial to their definition as chance events.
And, for many domains, the interactive nature of the relationship between an
agent and its environment may generate conceptual and modeling subtleties,
such as self-fulfilling and self-denying prophecies. If an agent is very
powerful relative to the other entities in its environment, as is the case for
a sole provider of a highly-demanded software product for instance, its
predictions of the future may be realized by the very nature of its
environmental power rather than being evidence of its prognostic capabilities.
Although of importance to rational agents or robots engaged in planning,
these examples demonstrate that the topic of Chance Discovery/ Management
covers a range of problems that have already arisen in other fields. Examples
include:
(a) demand forecasting for what marketers call "really new products";
(b) opportunity identification in business and marketing;
(c) hypothesis discovery in scientific theories;
(d) risk assessment and management in systems engineering, in environmental
management and in medicine;
(e) natural disaster prediction and management in public safety;
(f) identification of emergent behavior in systems of agents;
and
(g) the relationships between individual agents and the system in complex
systems theory.
However, the subject of Chance Discovery/Chance Management is not only concerned
with the techniques used in particular domains, but also with the overall
relationship of an agent with its environment as the two, working jointly,
discover and manage chance events. This holistic focus on the agent and its
environment as one, interacting, system is another point of difference between
the domain of Chance Discovery/Management and traditional approaches to
forecasting or pattern matching.
This proposed AAAI symposium will seek to bring together members of the AI
community with people from various application domains to share methods and
approaches to this set of problems. Because many of the techniques in risk
analysis, demand forecasting, etc, were first developed by applied
practitioners in industry and government, it is hoped that members of these
professional communities will also participate, in addition to the respective
academic communities.
Moreover, as well as the sharing of approaches from different disciplines, it
is hoped that the Symposium will encourage and facilitate alternative
formalizations of the problems of Chance Discovery/Management and their
possible solution. Formal methods of prediction and management of rare events
will be required if these techniques are to be adopted by planning agents and
robots acting in the world. Because our interest concerns the relationship
between an agent and its environment, research in human-computer interaction
(HCI) may also be relevant, since the design of an interface may facilitate (or
inhibit) the discovery and management of chance events.
Because this a new and multi-disciplinary domain, no regular conferences
or meetings devoted to it yet exist, although ad-hoc sessions on chance
discovery have been held in association with other international conferences.
Recently, a one-day international workshop on Chance Discovery was held as part
of the 15th Annual Meeting of the Japanese Society for AI (JSAI-2001), in
Matsue, Japan, in May 2001. This workshop was organized by one of the
co-chairs of this proposed AAAI Symposium, Yukio Ohsawa, and attracted 25
participants; a review of that meeting, written by Peter McBurney, will
appear shortly in the journal "Knowledge Engineering Review". This review is
available on line at:
http://www.csc.liv.ac.uk/~peter/cd2001.html
C. PARTICIPATION:
The organizers of this Symposium encourage participation from different
communities, both academic and industrial, including:
- - Artificial Intelligence (particularly the multi-agent systems and planning
communities)
- - Knowledge Discovery and Data Mining
- - Information retrieval and analysis
- - Human-Computer Interaction
- - WWW Awareness
- - Marketing theory and demand forecasting
- - Risk analysis, prediction and management
- - Social trends analysis
- - Social psychology
- - Management and decision sciences
- - Operations research
- - Statistics and data analysis
- - Complex systems theory and application
- - Philosophy of forecasting and risk.
D. FORMAT:
It is proposed that the format of the symposium will be a combination of:
- - 1 or 2 Invited talks (each c. 45 minutes, including discussion)
- - Presentations on contributed papers (c. 30 min)
- - For each presentation, a short critique by a reviewer, nominated before the
conference (10 min), The reviewer will have had time before the meeting to read
and reflect on the paper being critiqued, and so may be able to provide a more
considered response to it.
- - Shorter presentations of posters (5-10 min)
- - Panel discussions (c. 30 min)
- - Parallel break-out sessions to discuss particular problems, e.g.
forecasting demand for hi-tech products; risk analysis for complex systems;
integration of technical and social risk analysis (c. 60 min).
E. ORGANIZING COMMITTEE:
Yukio Ohsawa
Graduate School of Systems Management
University of Tsukuba
3-29-1 Otsuka, Bunkyo-kyu
Tokyo 112-0012
Japan
Tel: + 81 3 3942 7141
Email: osawa@gssm.otsuka.tsukuba.ac.jp
Simon Parsons
Agent Applications, Research and Technologies (Agent ART) Group
Department of Computer Science
University of Liverpool
Chadwick Building, Peach Street
Liverpool L69 7ZF
U. K.
Tel: + 44 151 794 6760
Email: s.d.parsons@csc.liv.ac.uk
Peter McBurney
Agent Applications, Research and Technologies (Agent ART) Group
Department of Computer Science
University of Liverpool
Chadwick Building, Peach Street
Liverpool L69 7ZF
U. K.
Tel: + 44 151 794 6768
Email: p.j.mcburney@csc.liv.ac.uk
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