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.

Incremental search reuses information from previous searches to find solutions to a series of similar search problems potentially faster than is possible by solving each search problem from scratch. This is important because many AI systems have to adapt their plans continuously to changes in (their knowledge of) the world. In this article, we give an overview of incremental search, focusing on LIFELONG PLANNING A*, and outline some of its possible applications in AI.

(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.

A method for unrestricted recognition of three-dimensional objects was developed. By unrestricted, we imply that the recognition will be done independently of object position, scale, orientation, and pose against a structured background. It does not assume any preceding segmentation or allow a reasonable degree of occlusion. The method uses a hierarchy of triplet feature invariants, which are at each level defined by a learning procedure. In the feedback learning procedure, percepts are mapped on system states corresponding to manipulation parameters of the object. The method uses a learning architecture with channel information representation. This article discusses how objects can be represented. We propose a structure to deal with object and contextual properties in a transparent manner.

An adequate natural language description of developments in a real-world scene can be taken as proof of "understanding what is going on." An algorithmic system that generates natural language descriptions from video recordings of road traffic scenes can be said to "understand" its input to the extent that algorithmically generated text is acceptable to the humans judging it. A fuzzy metrictemporal Horn logic (FMTHL) provides a formalism for representing both schematic and instantiated conceptual knowledge about the depicted scene and its temporal development. The resulting conceptual representation mediates in a systematic manner between the spatiotemporal geometric descriptions extracted from video input and a module that generates natural language text. This article outlines a 30-year effort to create such cognitive vision system, indicates its current status, summarizes lessons learned along the way, and discusses open problems against this background.

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.

"This book will appeal to technophiles, interdisciplinarians, and broad thinkers of all

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 build a machine that has "common sense" was once a principal goal in the field of artificial intelligence. But most researchers in recent years have retreated from that ambitious aim. Instead, each developed some special technique that could deal with some class of problem well, but does poorly at almost everything else. We are convinced, however, that no one such method will ever turn out to be "best," and that instead, the powerful AI systems of the future will use a diverse array of resources that, together, will deal with a great range of problems. To build a machine that's resourceful enough to have humanlike common sense, we must develop ways to combine the advantages of multiple methods to represent knowledge, multiple ways to make inferences, and multiple ways to learn. We held a two-day symposium in St. Thomas, U.S. Virgin Islands, to discuss such a project -- - to develop new architectural schemes that can bridge between different strategies and representations. This article reports on the events and ideas developed at this meeting and subsequent thoughts by the authors on how to make progress.

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.