SIGART Annual Report
July 2002 - June 2003
Submitted by: Joe Marks, Past SIGART Chair
The scope of SIGART consists of the study of intelligence and its realization in computer systems. Many important areas of computer science fall within SIGART's ambit, such as: autonomous agents, intelligent user interfaces, knowledge discovery and data mining, human-language technology, cognitive modeling, qualitative reasoning, knowledge representation, planning, scheduling, logic programming, problem solving, search, connectionist models, machine learning, robotics, and computer vision.
SIGART's mission is to support conferences and activities that advance the science and technology of AI. During 2003 and 2003 SIGART arranged to co-sponsor eight conference meetings and to cooperate with eight more. The current SIGART-sponsored conferences are AAMAS, K-CAP, IAT/WI, IUI, and ASE, with a collective attendance of over 1000 people. The Seventh SIGART/AAAI Doctoral Consortium (DC) was held in conjunction with the National Conference on Artificial Intelligence (AAAI-2002), which took place in July 2002 in Edmonton, Canada. The DC provided an opportunity for 13 Ph.D. students to discuss and explore their research interests and career objectives with a panel of established AI researchers. Finally, SIGART co-sponsored the SIGART Autonomous Agents Research Award that was presented at the AAMAS Conference in Melbourne, Australia. This award recognizes researchers in autonomous agents whose current and recent work is particularly influential. This year's winner was Prof. Nick Jennings of Southampton University, England.
Major Changes and Challenges:
After a two-year overhaul, SIGART has a new mission statement and operating rules, is financially secure, and is being run by an enlarged and reenergized group of volunteers. As part of this overhaul, SIGART switched to being a "conferences-only" SIG and adopted new operating rules and procedures. Under the operating rules, SIGART is now run by an Executive Board, comprising a Chair, Vice Chair, Treasurer, and Communications Officer. The Executive Board is responsible for the day-to-day running of the SIG, informed by the recommendations of an Advisory Board and subject to the by-laws of the ACM. The SIGART Advisory Board comprises representatives from the major conferences that SIGART sponsors currently: AAMAS, K-CAP, IAT/WI, IUI, ASE, and the SIGART/AAAI Doctoral Consortium. This new organization will ensure more and better communication between SIGART and its major conferences.
The overhaul of SIGART begun two years ago is not yet over. Three significant challenges remain:
Providing improved support to SIGART-sponsored conferences: It is very important that SIGART provides support commensurate to the fees and surpluses contributed by sponsored conferences. One very important step that was taken last year was to formally adopt a rule that 80% of a conference's surplus funds be spent to benefit that conference. Another step taken last year was to inaugurate a program of financial support for invited speakers at sponsored conferences. If finances allow it, a possible future step would be to provide scholarship funds for students to attend SIGART-sponsored conferences.
Expanding the AI content in the ACM Digital Library: The majority of SIGART-supported conferences have "in-cooperation" status. However, the majority of these conferences do not make their conference proceedings available to the ACM Digital Library (DL). We should do more to make the ACM Digital Library the central repository of AI-related literature. One possible step in this direction would be to offer a subsidy to "in-cooperation" conferences that use the ACM to publish their proceedings and thereby make their content available to the DL.
Improving the benefits of SIGART membership: Currently the main benefits of SIGART membership are reduced registration at all SIGART-supported conferences, and copies of the proceedings from the main SIGART-sponsored conferences. The latter benefit might be improved by amalgamating all the proceedings from one year into a single set of CDs or DVDs; an even better idea, adopted by some other ACM SIGs, might be to provide free access for members to the SIGART-contributed portions of the ACM Digital Library.
Written by leading domain experts for software engineers, ACM Case Studies provide an in-depth look at how software teams overcome specific challenges by implementing new technologies, adopting new practices, or a combination of both. Often through first-hand accounts, these pieces explore what the challenges were, the tools and techniques that were used to combat them, and the solution that was achieved.
ACM Queue’s “Research for Practice” is your number one resource for keeping up with emerging developments in the world of theory and applying them to the challenges you face on a daily basis. In this installment, Dan Crankshaw and Joey Gonzalez provide an overview of machine learning server systems. What happens when we wish to actually deploy a machine learning model to production, and how do we serve predictions with high accuracy and high computational efficiency? Dan and Joey’s curated research selection presents cutting-edge techniques spanning database-level integration, video processing, and prediction middleware. Given the explosion of interest in machine learning and its increasing impact on seemingly every application vertical, it's possible that systems such as these will become as commonplace as relational databases are today.