In an attempt to solve as much of the AAAI Robot Challenge as possible, five research institutions representing academia, industry, and government integrated their research into a single robot named GRACE. This article describes this first-year effort by the GRACE team, including not only the various techniques each participant brought to GRACE but also the difficult integration effort itself.

The purpose of the AAAI-2002 Robot Rescue event is to challenge researchers to design useful robotic systems for urban search and rescue (USAR). The competition rules are written to simulate a real rescue response in a simulated disaster environment developed by the National Institute of Standards and Technology. This article provides an overview of the current state of the art for USAR robotics, an overview of the AAAI-2002 Robot Rescue event, and a discussion of the future of the Robot Rescue event.

In the span of a few months, the AI community lost four important figures. The fall of 2002 marked the passing of Ray Reiter, for whom a memorial article by Jack Minker appears in this issue. As the issue was going to press, AI lost Saul Amarel, Norm Nielsen, and Charles Rosen. This section of AI Magazine commemorates these friends, leaders, and AI pioneers. We thank Tom Mitchell and Casimir Kulikowski for their memorial to Saul Amarel, Ray Perrault for his remembrance of Norm Nielsen, and Peter Hart and Nils Nilsson for their tribute to Charles Rosen. The AI community mourns our lost colleagues and gratefully remembers their contributions, which meant so much to so many and to the advancement of artificial intelligence as a whole.

Life meetings have grown from small www.comp.hkbu.edu.hk/IAT03/

However, even if you become aware of the value of a chance event, for example, with a new behavior of a customer in the market you are selling in, it is still hard to persuade your colleagues to make actions in response to the rare event. "Interesting keywords arose, such as "You had a symposium on the creation The Symposium on Etiquette for Human-Computer "So was it a conference on knowledge Work began its meeting--with discovery inviting philosophers?" The first invited talk In this symposium, we had 17 papers, Jeanne Comeau, an author, speaker, gave us deep insight into customer 2 invited lectures, and 14 other and teacher on etiquette and the director networks in the market, and the last speakers. Six countries (Japan, United of the Etiquette School of panel extended to management, persuasion, States, United Kingdom, Germany, Boston. Comeau taught us a great communication, and trust, Portugal, and the Czech Republic) deal about etiquette's history and and so on.

The Fifth International Symposium on Abstraction, Reformulation, and Approximation (SARA-2002) was held from 2 to 4 August 2002 in Kananaskis, Alberta, Canada. This interdisciplinary conference brought together researchers from around the world to present recent progress on, and exchange ideas about, how abstraction, reformulation, and approximation techniques can be used in areas such as automatic programming, constraint satisfaction, design, diagnosis, machine learning, search, planning, reasoning, game playing, scheduling, and theorem proving.

This article discusses our experience building and running an intelligent control system during a three-year period for a National Aeronautics and Space Administration advanced life support (ALS) system. The system under test was known as the Integrated Water-Recovery System (IWRS). We used the 3T intelligent control architecture to produce software that operated autonomously, 24 hours a day, 7 days a week, for 16 months. The article details our development approach, the successes and failures of the system, and our lessons learned. We conclude with a summary of spin-off benefits to the AI community and areas of AI research that can be useful for future ALS systems.

This eclectic keepsake provides a sampling of what can be found (with links to the full articles) on the AI Topics web site. Please keep in mind that (1) the mere mention of anything here does not imply any endorsement whatsoever; (2) the excerpt might not reflect the overall tenor of the article; (3) although the articles were initially available online and without charge, few things that good last forever; and (4) the AI in the News collection -- updated, hyperlinked, and archived -- can be found by going to www.aaai.org/aitopics/ html/current.php.

There is an increasing need for automated support for humans monitoring the activity of distributed teams of cooperating agents, both human and machine. We characterize the domain-independent challenges posed by this problem, and describe how properties of domains influence the challenges and their solutions. We will concentrate on dynamic, data-rich domains where humans are ultimately responsible for team behavior. Thus, the automated aid should interactively support effective and timely decision making by the human. We present a domain-independent categorization of the types of alerts a plan-based monitoring system might issue to a user, where each type generally requires different monitoring techniques. We describe a monitoring framework for integrating many domain-specific and task-specific monitoring techniques and then using the concept of value of an alert to avoid operator overload. We use this framework to describe an execution monitoring approach we have used to implement Execution Assistants (EAs) in two different dynamic, data-rich, real-world domains to assist a human in monitoring team behavior. One domain (Army small unit operations) has hundreds of mobile, geographically distributed agents, a combination of humans, robots, and vehicles. The other domain (teams of unmanned ground and air vehicles) has a handful of cooperating robots. Both domains involve unpredictable adversaries in the vicinity. Our approach customizes monitoring behavior for each specific task, plan, and situation, as well as for user preferences. Our EAs alert the human controller when reported events threaten plan execution or physically threaten team members. Alerts were generated in a timely manner without inundating the user with too many alerts (less than 10 percent of alerts are unwanted, as judged by domain experts).

The proliferation of online information sources has led to an increased use of wrappers for extracting data from Web sources. While most of the previous research has focused on quick and efficient generation of wrappers, the development of tools for wrapper maintenance has received less attention. This is an important research problem because Web sources often change in ways that prevent the wrappers from extracting data correctly. We present an efficient algorithm that learns structural information about data from positive examples alone. We describe how this information can be used for two wrapper maintenance applications: wrapper verification and reinduction. The wrapper verification system detects when a wrapper is not extracting correct data, usually because the Web source has changed its format. The reinduction algorithm automatically recovers from changes in the Web source by identifying data on Web pages so that a new wrapper may be generated for this source. To validate our approach, we monitored 27 wrappers over a period of a year. The verification algorithm correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes, resulting in precision of 0.73 and recall of 0.95. We validated the reinduction algorithm on ten Web sources. We were able to successfully reinduce the wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data extraction task.