The 2003International Conference on Automated Planning and Scheduling (ICAPS-03) was held 9 to 13 June 2003 in Trento, Italy. It was chaired by Enrico Giunchiglia (University of Genova), Nicola Muscettola (NASA Ames), and Dana Nau (University of Maryland). Piergiorgio Bertoli and Marco Benedetti (both from ITC-IRST) were the local chair and the workshop-tutorial coordination chair, respectively.

To submit a candidate's name for consideration, please send the individual's name, address, telephone number, and email address to Carol Nominators should contact candidates prior to submitting their names to verify that they are willing to serve, should they be elected. "AI in the News" section of the AI Intelligence will be held at 12:45 PM, October 22-24, 2004, at the Hyatt (see www.aaai.org/aitopics/assets/ If you are a Washington, DC. Cochairs: Simon that the use of such excerpts is past president. Ross Gayler personal and does not amount to, or year four new councilors are elected (r.gayler@mbox.com.au), and Pentti result in, commercial distribution.

(ICKEDS 2004). This book looks at some of the results of the synergy among AI, cognitive science, and education. Examples include virtual students whose misconceptions force students to reflect on their own knowledge, intelligent tutoring systems, and speech-recognition technology that helps students learn to read. Some of the systems described are already used in classrooms and have been evaluated; a few are still laboratory efforts. The book also addresses cultural and political issues involved in the deployment of new educational technologies.

This eclectic keepsake provides a sampling in action' for the first time. Its destruction "You may have read about the outsourcing of what can be found (with links to the full Please may well have been saved, the company today, in cover articles in Time, Wired, keep in mind that (1) the mere mention of said. 'It was a special moment--a robot Business Week.... In New Hampshire, John anything here does not imply any endorsement got blown up instead of a person,' said Kerry was asked about the problem. His whatsoever; (2) the excerpt might not iRobot CEO Colin Angle.... Between 50 answer: 'We have to create the next wave reflect the overall tenor of the article; (3) although and 100 PackBots are now being used in of those kinds of jobs that come from the the articles were initially available Iraq and Afghanistan for battlefield reconnaissance, fact that we're highly educated and deeply online and without charge, few things that "'Conscious robot is not an oxymoron -- Dial'em for Mumbai.

Ohio State University) focused on the use of case-based reasoning for both planning and world modeling. Nicola Muscettola (NASA Ames) focused on reactive behaviors. Laboratory) described an approach Within this general theme, to planning with multiagent the aim was to bring together researchers execution. The presentation ecent developments in multiagent shown promising results in the robotics, intelligent autonomous of Thomas Wagner (University of modeling of autonomous, collaborative vehicles). The common denominator Brement), Christoph Schlieder (University behavior between agents in different that these groups share is the pragmatic of Bamberg), and Ubbo Visser environments.

INTF-1 technical search team as it arrived on site and conducted a reconnaissance of the collapsed building. The scientists also got to observe the process by which the search team manager decided whether to use traditional tools, such as acoustic sensors or search cameras, or a robot (figure 2). The robots were deployed the National Science Foundation's Each scientist went into with rescue workers as they went the rubble at least two times and witnessed through a complete deploy-searchcleanup the deployment of each brand Search and Rescue (CRASAR) at cycle or "evolution." Embedding under such realistic conditions permitted the participants to gain an ethnographic understanding of rescue robotics, direct access to one type of collapse site, and an introduction to standard operating procedures such as decontaminating the robots that might impact the design of better robots and software. The scientists brought sleeping bags and slept during the single four-hour rest cycle allotted to the rescue workers.

The Twelfth Annual Association for the Advancement of Artificial Intelligence (AAAI) Robot Competition and Exhibition was held in Acapulco, Mexico, in conjunction with the Eighteenth International Joint Conference on Artificial Intelligence. The events included the Robot Host and Urban Search and Rescue competitions, the AAAI Robot Challenge, and the Robot Exhibition. In the Robot Host event, the robots had to act as mobile information servers and guides to the exhibit area of the conference. In the Urban Search and Rescue competition, teams attempted to find victims in a simulated disaster area using teleoperated, semiautonomous, and autonomous robots. The AAAI Robot Challenge is a noncompetitive event where the robots attempt to attend the conference by locating the registration booth, registering for the conference, and then giving a talk to an audience. Finally, the Robot Exhibition is an opportunity for robotics researchers to demonstrate their robots' capabilities to conference attendees. The three days of events were capped by the two Robot Challenge participants giving talks and answering questions from the audience.

NASA Ames Research Center Polish Academy of Sciences URL: www.taai.org.tw/announce/ (PRICAI 2004). (ICKEDS 2004). This book looks at some of the results of the synergy among AI, cognitive science, and education. Examples include virtual students whose misconceptions force students to reflect on their own knowledge, intelligent tutoring systems, and speech recognition technology that helps students learn to read.

This is the first of three planned papers describing ZAP, a satisfiability engine that substantially generalizes existing tools while retaining the performance characteristics of modern high-performance solvers. The fundamental idea underlying ZAP is that many problems passed to such engines contain rich internal structure that is obscured by the Boolean representation used; our goal is to define a representation in which this structure is apparent and can easily be exploited to improve computational performance. This paper is a survey of the work underlying ZAP, and discusses previous attempts to improve the performance of the Davis-Putnam-Logemann-Loveland algorithm by exploiting the structure of the problem being solved. We examine existing ideas including extensions of the Boolean language to allow cardinality constraints, pseudo-Boolean representations, symmetry, and a limited form of quantification. While this paper is intended as a survey, our research results are contained in the two subsequent articles, with the theoretical structure of ZAP described in the second paper in this series, and ZAP's implementation described in the third.

We describe a method for learning sparse multiscale image representations using a sparse prior distribution over the basis function coefficients. The prior consists of a mixture of a Gaussian and a Dirac delta function, and thus encourages coefficients to have exact zero values. Coefficients for an image are computed by sampling from the resulting posterior distribution with a Gibbs sampler. The learned basis is similar to the Steerable Pyramid basis, and yields slightly higher SNR for the same number of active coefficients. Denoising using the learned image model is demonstrated for some standard test images, with results that compare favorably with other denoising methods.