The RoboCupJunior division of RoboCup is now entering its third year of international participation and is growing rapidly in size and popularity. This article first outlines the history of the junior league since it was first demonstrated in Paris at RoboCup-1998 and describes how it has evolved into the international sensation it is today. Although the popularity of the event is self-evident, we are working to identify and quantify the educational benefits of the initiative. The remainder of the article focuses on describing our efforts to encapsulate these qualities, highlighting results from a pilot study conducted at RoboCupJunior-2000 and presenting new data from a subsequent study of RoboCupJunior-2001.

Competitions were held at Since the first competition in 1997 (Kitano Fukuoka Dome Baseball Stadium from 19 to 23 1998), RoboCup has grown into an international June followed by the International RoboCup joint research project in which about Symposium on 24 to 25 June. It is one of RoboCup is an attempt to foster intelligent the most ambitious projects of the twenty-first robotics research by providing a standard century. RoboCup currently consists of three problem, the ultimate goal of which is to divisions: (1) RoboCupSoccer, a move toward build a team of 11 humanoid robots that the final goal; (2) RoboCupRescue, a serious social can beat the human World Cup champion application of rescue activities for any kind soccer team by 2050. It's obvious that of disaster; and (3) RoboCupJunior, an international building a robot to play a soccer game is an education-based initiative designed to immense challenge; readers might therefore introduce young students to robotics. It is our intention to use since 1997 and showed its epoch-making new RoboCup as a vehicle to promote robotics standard for future RoboCups. One thousand and AI research by offering a publicly appealing four team members from 188 teams from 30 but formidable challenge (Asada et nations around the world participated. It included al. 1999; Kitano et al. 1997). The humanoid league is a big challenge knowledge, this was the largest robotic event with a long-term, high-impact goal, which in history.

Simulations are an excellent tool for studying AI. However, the simulation technology in use by, and designed for, the AI community often fails to take advantage of much of the work in the larger simulation community to produce stable, repeatable, and efficient simulations. I present SPADES (SYSTEM FOR PARALLEL-AGENT DISCRETE-EVENT SIMULATION) as a simulation substrate for the AI community. SPADES focuses on the agent as a fundamental simulation component. The "thinking time" of an agent is tracked and reflected in the results of the agents' actions. SPADES supports and manages the distribution of agents across machines while it is robust to variations in network performance and machine load. SPADES is not tied to any particular simulation and is a powerful new tool for creating simulations for the study of AI.

Robert S. (Bob) Engelmore, who retired in 1998 He When the HPP's goal shifted to studying information Allan Terry's of Technology (later Carnegie Mellon University) Ph.D. dissertation and several publications and became a physics major. He had close grew out of this work. Working with crystallographers friendships with (later-to-be AI scientists) Professor Joseph Kraut and Dr. Steve Robert Lindsay and Ed Feigenbaum and Freer from the University of California at San roomed with Feigenbaum for six years of undergraduate Diego, Bob and Allan designed and implemented and graduate school. It graduate work, he met his future wife, Ellie, in was an ambitious project, involving sophisticated Pittsburgh. They were married in 1958.

I'm delighted to bring our readers the news of an exciting resource for AAAI members. AAAI has now completed a major initiative, begun five years ago, to develop a digital library of AAAI publications. The collection now comprises approximately 13,000 papers, including the full set of papers from the AAAI proceedings, papers from other major conferences, AAAI workshop and symposium technical reports, selected AAAI Press books, and the full contents of AI Magazine. This already-extensive collection is a growing resource, with new publications and access methods to be added over time. I encourage readers to visit it at the members' library section of the AAAI web site, www.aaai.org.

Natural language generation (NLG) systems are computer software systems that produce texts in English and other human languages, often from non-linguistic input data. NLG systems, like most AI systems, need substantial amounts of knowledge. However, our experience in two NLG projects suggests that it is difficult to acquire correct knowledge for NLG systems; indeed, every knowledge acquisition (KA) technique we tried had significant problems. In general terms, these problems were due to the complexity, novelty, and poorly understood nature of the tasks our systems attempted, and were worsened by the fact that people write so differently. This meant in particular that corpus-based KA approaches suffered because it was impossible to assemble a sizable corpus of high-quality consistent manually written texts in our domains; and structured expert-oriented KA techniques suffered because experts disagreed and because we could not get enough information about special and unusual cases to build robust systems. We believe that such problems are likely to affect many other NLG systems as well. In the long term, we hope that new KA techniques may emerge to help NLG system builders. In the shorter term, we believe that understanding how individual KA techniques can fail, and using a mixture of different KA techniques with different strengths and weaknesses, can help developers acquire NLG knowledge that is mostly correct.

Bayesian belief networks have grown to prominence because they provide compact representations for many problems for which probabilistic inference is appropriate, and there are algorithms to exploit this compactness. The next step is to allow compact representations of the conditional probabilities of a variable given its parents. In this paper we present such a representation that exploits contextual independence in terms of parent contexts; which variables act as parents may depend on the value of other variables. The internal representation is in terms of contextual factors (confactors) that is simply a pair of a context and a table. The algorithm, contextual variable elimination, is based on the standard variable elimination algorithm that eliminates the non-query variables in turn, but when eliminating a variable, the tables that need to be multiplied can depend on the context. This algorithm reduces to standard variable elimination when there is no contextual independence structure to exploit. We show how this can be much more efficient than variable elimination when there is structure to exploit. We explain why this new method can exploit more structure than previous methods for structured belief network inference and an analogous algorithm that uses trees.

Although many algorithms have been designed to construct Bayesian network structures using different approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). Within the score+search paradigm, the dominant approach uses local search methods in the space of directed acyclic graphs (DAGs), where the usual choices for defining the elementary modifications (local changes) that can be applied are arc addition, arc deletion, and arc reversal. In this paper, we propose a new local search method that uses a different search space, and which takes account of the concept of equivalence between network structures: restricted acyclic partially directed graphs (RPDAGs). In this way, the number of different configurations of the search space is reduced, thus improving efficiency. Moreover, although the final result must necessarily be a local optimum given the nature of the search method, the topology of the new search space, which avoids making early decisions about the directions of the arcs, may help to find better local optima than those obtained by searching in the DAG space. Detailed results of the evaluation of the proposed search method on several test problems, including the well-known Alarm Monitoring System, are also presented.

This article summarizes 16 agent strategies that were designed for the 2002 Trading Agent Competition. Agent architects use numerous general-purpose AI techniques, including machine learning, planning, partially observable Markov decision processes, Monte Carlo simulations, and multiagent systems. Ultimately, the most successful agents were primarily heuristic based and domain specific.

Robots in the Robot Host competition, part of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002) Mobile Robot Competition faced two challenges: (1) a serving task that was similar to the Hors d'Oeuvres, Anyone? Both tasks required moving carefully among people, politely offering them information or hors d'oeuvres, recognizing when the people are making a request, and answering the request.