This article reports on the Sixth Robot World Cup Competition and Conference (RoboCup-2002) Fukuoka/Busan, which took place from 19 to 25 June in Fukuoka, Japan. It was the largest Robo- Cup since 1997 and held the first humanoid league competition in the world. Further, the first ROBOTREX (robot trade and exhibitions) was held with about 50 companies, universities, and institutes represented. To the best of our knowledge, this was the largest robotic event in history.

To overcome these limitations, we propose an algorithm, called the CONTRACTING CURVE DENSITY (CCD) algorithm, for fitting parametric curves to image data. The method neither assumes object-specific color distributions, or specific edge profiles, nor does it need threshold parameters. To separate adjacent regions, we use local criteria that are based on local image statistics. We apply the method to the problem of localizing the ball and show that the CCD algorithm reliably localizes the ball even in the presence of heavily changing illumination, strong clutter, specularity, partial occlusion, and texture.

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.

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.

All accepted papers will appear in the conference proceedings published by AAAI Press. Selected authors will be invited to submit extended versions of their Ingrid Russell, University of Hartford papers to a special issue of the International Journal on Artificial Intelligence Tools irussell@hartford.edu The papers Valerie Barr, Hofstra University should not exceed 5 pages and is due by October 24, 2003. All submissions will be done Zdravko Markov, Central Connecticut State electronically via FLAIRS web submission system, which will be available through University the conference website. Please consult the conference web page for details on paper submission.

Chair: Terry Payne (trp@ecs.soton.ac.uk) nators should contact candidates prior Tentative Organizing AI Alert newsletter, which highlights they be elected. The deadline for Committee: Lloyd Greenwald selected features from the "AI in the nominations is November 1, 2003. Please mark your calendars now for Stanford University. Be sure Symposia/symposia.html) and will be and the Sixteenth Innovative Applications to visit the AI Topics web site at mailed to all AAAI members. Submissions of Artificial Intelligence Conference www.aaai.org/AITopics/aitopics.html will be due to the organizers on (IAAI-04)!

Successes and Challenges" by Alon Halevy, University of Washington; "Constraint Satisfaction, Santa Cruz; "Self-Reconfiguring Robots: Challenges and Successes" by Daniela Rus, Dartmouth University; "Automated Verification Graphs, Automata, and Logic" by Moshe Vardi, "Quantum Information: Fundamentals the world have gathered each summer One hundred the conference is always after conference hours. Also among this year's invited As part of the American Association As In addition to the strong technical to advance the science and practice part of a special track on AI and the track of the conference, the Fifteenth of AI, the organization continues web, Henzinger will discuss the future Innovative Applications of AI conference to play a leading role in organizing of search engines on the internet, (IAAI-03) will be collocated with and sponsoring these annual summer describing work under way to IJCAI. Through the years, we have conferences. Other invited talks include "Deploying in a broad range of computer systems, Information Agents on the machinery, and electronic devices Because AI is an umbrella term for a of Southern California; "Web However, where is the newest, most This book looks at some of the results of this 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 article reports on an extensive survey and analysis of research work related to machine learning as it applies to automated planning over the past 30 years. Major research contributions are broadly characterized by learning method and then descriptive subcategories. Survey results reveal learning techniques that have extensively been applied and a number that have received scant attention. We extend the survey analysis to suggest promising avenues for future research in learning based on both previous experience and current needs in the planning community.

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.

Hence, no training phase is needed. The local statistics define an with white lines; goals are blue and yellow; and expectation of "how the two sides of the curve robots are black with light blue or magenta might look." Second, refine the estimation of model parameters These stringent rules allow for simple mechanisms by (1) updating the mean of the estimation for object detection and recognition: in a maximum a posteriori step such that Segment the captured image into blobs of the the vicinity of the curve matches the expectation same color and interpret these blobs. To the defined by the local statistics and (2) updating best of our knowledge, all autonomous robot the covariance of the estimation based on soccer teams with vision-based perception apply the Hessian of the resulting objective function. However, because The two steps are repeated until there is no the RoboCup committee is planning to significant change in the estimated Gaussian make the rules more realistic, these objectrecognition distribution.