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.

(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.

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.

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.

Fifteen scientists from six universities and five companies were embedded with a team of search and rescue professionals from the Federal Emergency Management Agency's Indiana Task Force 1 in August 2003 at a demolished building in Lebanon, Indiana. The highly realistic 27-hour exercise enabled participants to identify the prevailing issues in rescue robotics. Perception and situation awareness were deemed the most pressing problems, with a recommendation to focus on human-computer cooperative algorithms because recognition in dense rubble appears far beyond the capabilities of computer vision for the near term. Human-robot interaction was cited as another critical area as well as the general problem of how the robot can maintain communications with the rescuers. The field exercise was part of an ongoing grant from the National Science Foundation to the Center for Robot-Assisted Search and Rescue CRASAR), and CRASAR is sponsoring similar activities in summer 2004.

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.

Identity uncertainty is a pervasive problem in real-world data analysis. It arises whenever objects are not labeled with unique identifiers or when those identifiers may not be perceived perfectly. In such cases, two observations mayor may not correspond to the same object. In this paper, we consider the problem in the context of citation matching--the problem ofdeciding which citations correspond to the same publication. Our approach is based on the use of a relational probability model to define a generative model for the domain, including models of author and title corruption and a probabilistic citation grammar. Identity uncertainty is handled by extending standard models to incorporate probabilities over the possible mappings between terms in the language and objects in the domain. Inference is based on Markov chain Monte Carlo, augmented with specific methods for generating efficient proposals when the domain contains many objects. Results on several citation data sets show that the method outperforms current algorithms for citation matching. The declarative, relational nature of the model also means that our algorithm can determine object characteristics such as author names by combining multiple citations of multiple papers.

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.

The focus of the paper is the problem of learning kernel operators from empirical data. We cast the kernel design problem as the construction of an accurate kernel from simple (and less accurate) base kernels. We use the boosting paradigm to perform the kernel construction process. To do so, we modify the booster so as to accommodate kernel operators. We also devise an efficient weak-learner for simple kernels that is based on generalized eigen vector decomposition. We demonstrate the effectiveness of our approach on synthetic data and on the USPS dataset. On the USPS dataset, the performance of the Perceptron algorithm with learned kernels is systematically better than a fixed RBF kernel.