Individual agent skills, such as kicking and dribbling (running with the ball), are important prerequisites for team collaboration. For each of these skills, many parameters affect the details of the skill execution. For example, in the ball skill of dribbling, there are parameters that affect how quickly the agent runs, how far ahead it kicks the ball, and on which side of its body the agent keeps the ball while it dribbles. The settings for these parameters usually involve a tradeoff, such as speed versus safety or power versus accuracy. It is important to gain an understanding of what exactly these tradeoffs are before "correct" parameter settings can be made. We created a trainer client that connects to the server as an omniscient offline coach client.

The Robot World Cup Soccer Games and Conferences (RoboCup) are a series of competitions and events designed to promote the full integration of AI and robotics research. Following the first RoboCup, held in Nagoya, Japan, in 1997, RoboCup-98 was held in Paris from 2-9 July, overlapping with the real World Cup soccer competition. RoboCup-98 included competitions in three leagues: (1) the simulation league, (2) the real robot small-size league, and (3) the real robot middle-size league. It was organized by University of Paris-VI and the Centre National de la Recherche Scientifique and was sponsored by Sony Corporation, NAMCO Limited, and SUNX Limited, with official balls for the middle-size league supplied by Molten Corporation. Over 15,000 people watched the games, and over 120 international media (such as CNN, ABC, NHK, and TV-Aich) and prominent scientific magazines covered the competition.

In a complex, dynamic multiagent setting, coherent team actions are often jeopardized by conflicts in agents' beliefs, plans, and actions. Despite the considerable progress in teamwork research, the challenge of intrateam conflict resolution has remained largely unaddressed. In these applications, agents must act together despite the uncertainties of their complex dynamic environment. Considerable progress has indeed been made in teamwork research. For example, recent advances in teamwork models (Tambe 1997; Jennings 1995), which explicitly outline agents' commitments and responsibilities in teamwork, have significantly improved flexibility in teamwork coordination and communication.

Disaster rescue is one of the most serious social issues that involves very large numbers of heterogeneous agents in the hostile environment. The intention of the RoboCup Rescue project is to promote research and development in this socially significant domain at various levels, involving multiagent teamwork coordination, physical agents for search and rescue, information infrastructures, personal digital assistants, a standard simulator and decision-support systems, evaluation benchmarks for rescue strategies, and robotic systems that are all integrated into a comprehensive system in the future. For this effort, which was built on the success of the RoboCup Soccer project, we will provide forums of technical discussions and competitive evaluations for researchers and practitioners. Although the rescue domain is intuitively appealing as a large-scale multiagent and intelligent system domain, analysis has not yet revealed its domain characteristics. The first research evaluation meeting will be held at RoboCup-2001, in conjunction with the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-2001), as part of the RoboCup Rescue Simulation League and RoboCup/AAAI Rescue Robot Competition.

In (automated) negotiation systems for self-interested agents, contracts have traditionally been binding. They do not accommodate future events. Contingency contracts address this but are often impractical. As an alternative, we propose leveledcommitment contracts. The level of commitment is set by decommitting penalties.

We describe one such competition, the Trading Agent Competition for Supply Chain Management (TAC SCM). We discuss its significance in the context of today's global market economy as well as AI research, the ways in which it breaks away from limiting assumptions made in prior work, and some of the advances it has engendered over the past six years. TAC SCM requires autonomous supply chain entities, modeled as agents, to coordinate their internal operations while concurrently trading in multiple dynamic and highly competitive markets. Since its introduction in 2003, the competition has attracted more than 150 entries and brought together researchers from AI and beyond in the form of 75 competing teams from 25 different countries. Yet the real-time demands of many domains do not lend themselves to traditional assumptions of rationality (Simon 1979, Wellman 1996).

The Robot World-Cup Soccer (RoboCup) is an attempt to foster AI and intelligent robotics research by providing a standard problem where a wide range of technologies can be integrated and examined. The first RoboCup competition will be held at the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan. A robot team must actually perform a soccer game, incorporating various technologies, including design principles of autonomous agents, multiagent collaboration, strategy acquisition, real-time reasoning, robotics, and sensor fusion. RoboCup is a task for a team of multiple fast-moving robots under a dynamic environment. Although RoboCup's final target is a world cup with real robots, RoboCup offers a software platform for research on the software aspects of RoboCup.

The American Association for Artificial Intelligence held its 1996 Spring Symposia Series on March 27 to 29 at Stanford University. This article contains summaries of the eight symposia that were conducted: (1) Acquisition, Learning, and Demonstration: Automating Tasks for Users; (2) Adaptation, Coevolution, and Learning in Multiagent Systems; (3) Artificial Intelligence in Medicine: Applications of Current Technologies; (4) Cognitive and Computational Models of Spatial Representation; (5) Computational Implicature: Computational Approaches to Interpreting and Generating Conversational Implicature; (6) Computational Issues in Learning Models of Dynamic Systems; (7) Machine Learning in Information Access; and (8) Planning with Incomplete Information for Robot Problems. This article contains summaries of the eight symposia that were conducted: (1) Acquisition, Learning, and Demonstration: Automating Tasks for Users; (2) Adaptation, Coevolution, and Learning in Multiagent Systems; (3) Artificial Intelligence in Medicine: Applications of Current Technologies; (4) Cognitive and Computational Models of Spatial Representation; (5) Computational Implicature: Computational Approaches to Interpreting and Generating Conversational Implicature; (6) Computational Issues in Learning Models of Dynamic Systems; (7) Machine Learning in Information Access; and (8) Planning with Incomplete Information for Robot Problems. This symposium brought together three different communities that are all looking at the problem of automating tasks through interactions with users: First, knowledge acquisition concentrates on how to structure a system's interactions with users based on the nature of the task to be automated. Second, machine learning seeks automated algorithms that do explanation or induction based on a user's actions.

This article presents the results of a multifaceted research and development effort that synergistically integrates AI research with military strategy research and practical deployment of agents into education. A distinguishing feature of this collaboration is the synergistic integration of AI research with military strategy research and the practical use of agents in education, as detailed in the following. View on the Evolution of the Software Development Process. strategic leaders at all the United States senior military service colleges, there is a great emphasis on the center of gravity analysis (Strange 1996). Hence, we have the third objective of this research, the educational objective of enhancing the educational process of senior military officers through the use of intelligent agent technology. Both programs emphasized the use of innovative challenge problems to focus and evaluate the research and development efforts.

KI is the main German national conference in AI, but it addresses an international audience by adopting English as the conference language and having the proceedings published in the Springer Lecture Notes in AI series (Jarke, Koehler, and Lakemeyer 2002). Of the 58 submissions from 17 countries, 20 were selected for presentation by the program committee, chaired by Jana Koehler, IBM Zurich, and Gerhard Lakemeyer, RWTH Aachen. Matthias Jarke, RWTH Aachen, was the general chair. The papers covered a broad range of areas, including multiagent systems, machine learning, natural language processing, constraint reasoning, knowledge representation, planning, and temporal reasoning. The paper by Franz Baader and Anni-Yasmin Turhan, TU Dresden, "On the Problem of Computing Small Representations of Least Common Subsumers," received the best paper award, sponsored by Springer-Verlag.