August 1-2, 1999, Stockholm, Sweden.
More Detais: http://www.aiai.ed.ac.uk/~paj/ijcai-wflow-wshop/
BACKGROUND
Events such as the "AI meets the Real World'' Workshop recently held in Newark, New Jersey have shown that a number of AI technologies have reached a level of maturity which sees them being used to solve a number
of real world tasks in companies such as Boeing, NASA, Lucent and Lockheed Martin. However, while there have been a number of notable successes the impact of AI technologies has been less than expected. One area which seems ripe for exploitation is process automation or workflow which is at the heart of many organizations and businesses. Workflow Management Systems are integrated software tools for supporting the modeling, analysis, and enactment of business process. The technology's development has been driven by the move to process oriented management in the 90s via initiatives like "Continuous Business Process Improvement'' and "Business Process re-engineering''. The market for workflow management software had grown from around $100 million in 1991 to $2.5 billion in 1996. From a research perspective DARPA has identified workflow as one of its key "must have" technologies and is investing heavily in developing the next generation workflow systems for the military. However, the impact of such systems will go well beyond the military and will be of great interest to the general business community. The Object Management Group has recently established an industry standard for interoperability of workflow systems, opening the door to enterprise-level and inter-enterprise process automation. Such standards will promote the development of specialized workflow systems and components incorporating AI functionality.
Conventional workflow management systems use explicit models and representations of process, along with automated tools that support the activation and ongoing management of a process instance. This technology has to date found application only in areas characterized by simple administrative type processes such as insurance claim processing. The benefits alluded to by workflow technology are highly desirable and the workflow research community has set the agenda of developing techniques that enable these benefits to be achieved in applications characterized by complex tasks performed in dynamic and uncertain environments. These are precisely the classes of tasks and environments that AI research has been investigating in the context of controlling computational entities and physical devices.
While workflow has emerged over the last eight years the artificial intelligence (AI) community has been involved with related research on process management for several decades. In contrast to workflow's focus on business and manufacturing processes, the AI community has been motivated primarily by domains that involve reactive control of computational entities and physical devices (e.g., robots, antennas, satellites, computer networks, agent communities). Despite these differing concerns and perspectives, there is much overlap between the objectives, requirements, and approaches of these two communities. Workflow provides the business drivers and the computational infrastructure that respectively motivate and enable an industrial deployment of AI technology. Such application will further develop the commercial credibility of AI technology while simultaneously providing immediate feedback to shape research programs.
GOALS AND FOCUS
The thesis underlying this workshop is that the AI community could be leveraged to realize a vision for dynamic process management, at both the modeling and technological levels. The objective of this Workshop, is to bring together researchers, practitioners, and applied AI specialists from diverse fields to discuss issues and emerging technologies for developing workflow and process management systems. In particular, we hope the workshop will address innovative ways of putting intelligence into Workflow Management to Revolutionize Business. The following are a partial list of AI disciplines that are relative to the workshop theme: Planning and reactive control and their architecture Multiagent planning architectures Distributed and continuous planning techniques Distributed AI Scheduling Knowledge acquisition Knowledge representation and reasoning Knowledge based and expert systems. Reflection and meta-level architecture Machine learning and adaptive system techniques Knowledge management
Topics of interest include (but are not limited to):
Definitions, terminology, and theoretical foundation for intelligent workflow management systems Knowledge representation schemes/techniques for building intelligent workflow management systems Incremental declarative specification and flexible coordination of workflow activities Handling uncertainties in workflow applications Deriving active rules for workflow enactment Performance engineering of human and computerized workflow management systems Reasoning over workflow activities and processes (Knowledge Based Workflow Management) Workflow Agents (Intelligent Agents that monitor/activate workflow processes and activities Innovative applications of intelligent workflow systems Workflow management systems that get smarter over time based on usage patterns Knowledge management activities that support workflow management
ORGANIZING COMMITTEE
Brian Drabble (co-chair), Computational Intelligence Research Laboratory, 1269 University of Oregon, Eugene, OR 97403-1269, USA. Tel: +1-541 346 0470 Fax: +1-541 346 0474
Mamdouh Ibrahim (co-chair), EDS, Leading Technologies and Methods, 5555 New King Street, Troy, MI 48098, USA Tel: +1-248-696-7129 Fax: +1-248-696-2325
Pauline Berry, SRI International Christoph Bussler, Boeing Research & Technology Fred Cummins, EDS/Leading Technologies and Methods Rose Gamble, Tulsa University Peter Jarvis, Artificial Intelligence Applications Institute, The University of Edinburgh Steve Marney, EDS Intelligent & Object Systems Karen L. Myers, SRI International Dan O'Leary, University of Southern California Santanu Paul, IBM TJ Watson Research
PROGRAM COMMITTEE
Ruth Aylett (The Information Technology Institute, Salford University, UK) Abraham Bernstein (Sloan School of Management, MIT, USA.) Paul Chung (Chemical Engineering Department, Loughborough University, UK) Barbara Dellen (Expert Systems Group, University of Kaiserslautern,
Germany) Sigrid Goldmann (Expert Systems Group, University of Kaiserslautern, Germany) Marie desJardins (SRI International, USA) Tom Garvey (SRI International, USA) Mark Klein (Center for Coordination Science, MIT, USA) Terri Lydiard (IBM, Global Services, UK) Azad Madni (Intelligent Systems Technology, Inc, USA) Ann Macintosh (Artificial Intelligence Applications Institute, Division of Informatics, The University of Edinburgh, UK) Jonathan Moore (Chemical Engineering Department, Loughborough University, UK) Charles Petrie (Center for Design Research, Stanford University, USA) Jussi Stader (Artificial Intelligence Applications Institute, Division of Informatics, The University of Edinburgh, UK) Austin Tate (Artificial Intelligence Applications Institute,
IJCAI-99 Conference Details
This workshop will be held as part of the Sixteenth International Joint Conference Artificial Intelligence (IJCAI-99) in Stockholm, Sweden during late July and early August 1999. Please see the conference site for more information.
Regards
Peter
-- Dr Peter Jarvis Research Scientist, Division of Informatics, The University of Edinburgh, 80 South Bridge, Edinburgh, EH1 1HN, UK. Web: http://www.aiai.ed.ac.uk/~paj/, e-mail: Peter.Jarvis@ed.ac.uk, tel: +44 (0)131 650 2756, fax: +44 (0)131 650 6513.-- See <http://www.cs.umbc.edu/agentslist> for list info & archives.
----------------------------------- Sr. Research Scientist Stanford Center for Design Research http://cdr.stanford.edu/~petrie/bio.html -----------------------------------
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