Keynote and Invited Speakers

• Prof. John McCarthy
  Stanford University, USA
  http://www-formal.stanford.edu/jmc/

• Prof. Boi B. Faltings
  Swiss Federal Institute of Technology, Switzerland
  http://liawww.epfl.ch/~faltings/

• Prof. Carl Kesselman
  University of Southern California, USA
  http://www.isi.edu/~carl/

• Prof. Victor Lesser
  University of Massachusetts, USA
  http://dis.cs.umass.edu/lesser.html

• Prof. Tom Mitchell
  Carnegie Mellon University, USA
  http://www-2.cs.cmu.edu/~tom/

• Prof. Xindong Wu
  University of Vermont, USA
  http://www.cs.uvm.edu/~xwu/home.html

Abstracts and Speaker Biographies

Features of today's Web environment were advocated and predicted as early as 1945 (Vannevar Bush) followed by McCarthy , J. C. R. Licklider, and Douglas Engelbart.

Technology has also been important (the stored program computer itself), time-sharing, CRT displays and now flat panels, the mouse, the internet, the personal computer (with all its troubbles), the Web, and adequate search engines.

We compare what was advocated and predicted with the present interactive environment. Some things came out better than predicted, and some things that were advocated have still not been realized.

Beyond what was predicted, there are still more possibilities, especially some involving artificial intelligence (e.g. understanding a confused user's state of mind), but also some involving (mere) hardware, e.g. the pocket computer with adequaate display and input.
Some of the present problems are institutional - combining making sure that authors get paid with universal access to their works.

John McCarthy is Professor of Computer Science at Stanford University. He has been interested in artificial intelligence since 1948 and coined the term in 1955. His main artificial intelligence research area has been the formalization of common sense knowledge. He invented the LISP programming language in 1958, developed the concept of time-sharing in the late fifties and early sixties, and has worked on proving that computer programs meet their specifications since the early sixties. He invented the circumscription method of non-monotonic reasoning in 1978.

His main research (1995) is formalizing common sense knowledge and reasoning. His articles are on John McCarthy's main web page (http://www-formal.stanford.edu/jmc/).

McCarthy received the A. M. Turing award of the Association for Computing Machinery in 1971 and was elected President of the American Association for Artificial Intelligence for 1983-84 and is a Fellow of that organization. He received the first Research Excellence Award of the International Joint Conference on Artificial Intelligence in 1985, the Kyoto Prize of the Inamori Foundation in November 1988, and the National Medal of Science in 1990. He is a member of the American Academy of Arts and Sciences, the National Academy of Engineering and the National Academy of Sciences. He has received honorary degree from Linkoping University in Sweden, the Polytechnic University of Madrid, Colby College, Trinity College, Dublin and Concordia University in Montreal, Canada. He has been declared a Distinguished Alumnus by the California Institute of Technology.

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Many situations present a social choice problem where different self-interested agents have to agree on joint, coordinated decisions. For example, power companies have to agree on how to use the power grid, and airlines have to agree on how to schedule takeoffs and landings.

Mechanisms for social choice are called incentive-compatible when cooperative behavior is optimal for all parties. The most well-known examples of incentive-compatible mechanisms are auctions. However, the party that receives the auction revenue has an incentive to manipulate the outcome to increase the revenue. For example, a power grid operator has an interest to reduce capacity and drive up prices.

Boi B. Faltings is the founder and director of the Artificial Intelligence Laboratory, and a full professor in the EPFL computer science department. His research interest is in Artificial Intelligence, in particular in constraint- and case-based reasoning and applications in software agents. Besides teaching and research at EPFL, Dr. Faltings serves the AI community as member of several editorial boards (AI Journal, AI Magazine, AI Communications, Constraints, and others) as well as by regular participation on conference committees (IJCAI, AAAI, ECAI, and others). He is a fellow of ECCAI, the European Coordinating Committee for Artificial Intelligence.

Dr. Faltings obtained his undergraduate degree at the Swiss Federal Institute of Technology, Zurich (ETH) in 1983 and a Ph.D. degree from the University of Illinois, Urbana, in 1987. From 1987 to 1993, he has been professeur extraordinaire at EPFL. Since 1993, he is full professor and have served as head of the computer science department from 1996-1998. He was visiting professor at NEC research institute in 1996 and Stanford University in 2001. He was a founder of Iconomic Systems, a company developing innovative solutions for travel e-commerce.

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Grids are a distributed computing technology whose objective is to provide the basic mechanisms for forming and operating dynamic distributed collaborations, or virtual organizations as they are sometimes called. While Grid infrastructure has focused on such things as the means for discovering and monitoring dynamic services, managing faults and failures, creating and managing service level agreements, creating and enforcing dynamic policy, to name a few --  to date, only limited progress has been made on creating the higher level reactive behaviors that would enable truly dynamic formation of virtual organizations. What is needed are the basic algorithms that enable independently operating entities to interact with one another with partial knowledge and have emerge a robust desirable behavior. This is exactly the range of problems that are being addressed by intelligent agent technologies. Hence, it seems likely that agent technology will play an important role in the development of the Grid as a pervasive infrastructure and the Grid offers an exciting range of
new applications for agents. In this talk I will explore the relationship of intelligent agents to the Grid and in particular focus on how agent technology can be applied to some specific challenges faced by Grid infrastructure and applications.

Dr. Carl Kesselman is Fellow in the Information Sciences Institute at the University of Southern California. He is the Director of the Center for Grid Technologies at the Information Sciences Institute and a Research Associate Professor of Computer Science at the University of Southern California. He received a Ph.D. in Computer Science from the University of California, Los Angeles, a Master of Science degree in Electrical Engineering from the University of Southern California, and Bachelors degrees in Electrical Engineering and Computer Science from the University at Buffalo.

Dr. Kesselman's current research interests are all aspects of Grid computing, including basic infrastructure, security, resource management, high-level services and Grid applications. He is the author of many significant papers in the field. Together with Dr. Ian Foster, he co-leads the Globus Project, one of the leading Grid research projects. The Globus project has developed the Globus Toolkit, the de facto standard for Grid computing.

Dr. Kesselman received the 1997 Global Information Infrastructure Next Generation Internet award, the 2002 R&D 100 award, the 2002 R&D Editors choice award, the Federal Laboratory Consortium (FLC) Award for Excellence in Technology Transfer and the 2002 Ada Lovelace Medal from the British Computing Society for significant contributions to information technology. Along with his colleagues Ian Foster and Steve Tuecke, he was named one of the top 10 innovators of 2002 by InfoWorld Magazine. In 2003, he and Dr. Foster were named by MIT Technology Review as the creators of one of the "10 technologies that will change the world."

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Over the last four years, I have been working on the design of a distributed sensor network for vehicle tracking using adaptive radar sensors. This effort has re-ignited my long-term interest in the use of organizational structuring for handling scaling issues in multi-agent systems. In this lecture, I will first examine how organizational structuring was used in our design to reduce the computational and communication load involved in coordinating agent activities, and I will discuss experiments that indicate the complex set of issues that need to be considered in evaluating the effectiveness of different organizational variants. I will also briefly illustrate how it is possible to model the performance of these variants analytically, and predict their performance. I will then present bottom-up techniques, based on negotiation, for organizational instantiation and adaptation in a dynamic environment. Finally, I will discuss some recent work on a knowledge-based top-down approach for organizational design.

Victor R. Lesser received his B.A. in Mathematics from Cornell University in 1966, and the M.S. and Ph.D. degrees in Computer Science from Stanford University in 1969 and 1972, respectively. He then joined CMU as member of the research faculty. Since 1977, he has been a Professor of Computer Science at the University of Massachusetts, Amherst. He is a founding fellow of AAAI and is considered a leading researcher in the areas of multi-agent systems (he is one of the founders of the field), real-time AI, and blackboard systems (he was the system architect for the Hearsay-II speech understanding system which was the first blackboard system developed). Professor Lesser has been very active in helping to organize and promote the field of Multi-Agent Systems. He was General Chairman of the First International Conference on Multi-Agent Systems and was the founding president of the International Foundation for Multi-Agent Systems.

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Many researchers envision a future of intelligent, personalized agents in their computer workstations. These agents could provide a variety of personalized assistance helping to manage email, schedule meetings, gather and summarize relevant information, track progress of various projects, etc.

Perhaps the greatest barrier to creating such intelligent agents is that the data observable to such an agent is unstructured and difficult to automatically interpret -- it includes unstructured workstation files (text, images, and other formats), email, calendar entries, web accesses, etc. This talk will discuss current research toward such intelligent workstation agents, and in particular toward making this unstructured data understandable to computer agents. This research is being conducted as part of a multi-university research effort on intelligent personalized assistants.

Tom M. Mitchell is the Fredkin Professor of Computer Science at Carnegie Mellon University. His research lies in the area of machine learning, data mining, artificial intelligence, and information fusion. Mitchell is author of the textbook "Machine Learning," Past President of the American Association of Artificial Intelligence (AAAI), and a member of the US National Research Council's Computer Science and Telecommunications Board. In 2002 he received the Debye Prize from the Edmund Hustinx Foundation for his research in computer science. Mitchell is the founding director of CMU's Center for Automated Learning and Discovery, an interdisciplinary research center specializing in statistical machine learning and data mining, and the first institution to offer a Ph.D. program specifically in this area. Mitchell's recent research has focused on machine learning approaches to analyzing human brain function based on fMRI data, and on machine learning for intelligent personal assistants.

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Data mining is a fast-growing area. The first Knowledge Discovery in Databases Workshop was held in August 1989, in conjunction with the 1989 International Joint Conference on Artificial Intelligence, and this workshop series became the International Conference on Knowledge Discovery and Data Mining (KDD) in 1995. In 2003, there were a total of 15 data mining conferences, most of which are listed at http://www.kdnuggets.com/meetings/meetings-2003-past.html.

These 15 conferences do not include various artificial intelligence (AI), statistics and database conferences (and their workshops) that also solicited and accepted data mining related papers, such as IJCAI, ICML, ICTAI, COMPSTAT, AI & Statistics, SIGMOD, VLDB, ICDE, and CIKM.

Among various data mining conferences, KDD and ICDM (the IEEE International Conference on Data Mining) are arguably (or unarguably) the two premier ones in the field. ICDM was established in 2000, sponsored by the IEEE Computer Society, and had its first annual meeting in 2001.

This talk will review the topics of interest from ICDM from an AI perspective, and analyze common topics in data mining and AI, including key AI ideas that have been used in both data mining and machine learning. We will also discuss two current research projects on (1) user-centered agents for biological information exploration on the Web, and (2) dynamic classifier selection in dealing with streaming data. Both projects apply data mining techniques for intelligent analysis of large volumes of data.

Xindong Wu is Professor and Chair of the Department of Computer Science at the University of Vermont, USA. He holds a PhD in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He has published extensively in these areas in various journals and conferences, including IEEE TKDE, TPAMI, ACM TOIS, IJCAI, AAAI, ICML, KDD, ICDM, and WWW.

Dr. Wu is the Executive Editor of Knowledge and Information Systems (a peer-reviewed archival journal published by Springer-Verlag), the founder and current Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), a Series Editor of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP), and the Chair of the IEEE Computer Society Technical Committee on Computational Intelligence (TCCI).

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