Received: from jiscmail.ac.uk (jiscmail.ac.uk [130.246.192.48]) by firat.bcc.bilkent.edu.tr (8.11.4/8.11.4) with ESMTP id f83DMb021966 for <[log in to unmask]>; Mon, 3 Sep 2001 16:22:38 +0300 (EET DST) Received: from jiscmail (jiscmail.ac.uk) by jiscmail.ac.uk (LSMTP for Windows NT v1.1b) with SMTP id <[log in to unmask]>; Mon, 3 Sep 2001 14:19:51 +0100 Received: from JISCMAIL.AC.UK by JISCMAIL.AC.UK (LISTSERV-TCP/IP release 1.8d) with spool id 5990814 for [log in to unmask]; Mon, 3 Sep 2001 14:19:50 +0100 Received: from jiscmail (jiscmail.ac.uk) by jiscmail.ac.uk (LSMTP for Windows NT v1.1b) with SMTP id <[log in to unmask]>; Mon, 3 Sep 2001 14:19:50 +0100 Message-ID: <[log in to unmask]> Date: Mon, 3 Sep 2001 14:19:50 +0100 Reply-To: Peter McBurney <[log in to unmask]> Sender: "This list is operated in collaboration with Risk, Decision and Policy, publ" <[log in to unmask]> From: Peter McBurney <[log in to unmask]> Subject: Fwd: Call for Expressions of Interest -- Chance Discovery To: [log in to unmask] X-Virus-Scanned: by AMaViS perl-11 X-Status: X-Keywords: X-UID: 3 X-Mozilla-Status2: 00000000 (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: [log in to unmask] With thanks, Yukio Ohsawa, Simon Parsons and Peter McBurney **************************************************************** 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: [log in to unmask] Web page: www.csc.liv.ac.uk/~peter/ ***************************************************************************** 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: [log in to unmask] 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: [log in to unmask] 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: [log in to unmask] **************************************************************************