The innovative reapplication of a multiagent system for human-in-the-loop (HIL) simulation was a consequence of appropriate agent-oriented design. The use of intelligent agents for simulating human decision making offers the potential for analysis and design methodologies that do not distinguish between agent and human until implementation. With this as a driver in the design process, the construction of systems in which humans and agents can be interchanged is simplified. Two systems have been constructed and deployed to provide defense analysts with the tools required to advise and assist the Australian Defense Force in the conduct of maritime surveillance and patrol. The experiences gained from this process indicate that it is simpler, both in design and implementation, to add humans to a system designed for intelligent agents than it is to add intelligent agents to a system designed for humans.

The Second International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-03) was held in Melbourne, Australia, in July 2003. Attracting nearly 500 delegates, the event confirmed AAMAS as the academic main event for researchers with an interest in multiagent systems. We summarize the conference highlights and report on the associated workshops, tutorials, and emerging trends. Although a number of workshops had been held more or less regularly since 1980 (notably the U.S.-based Distributed Artificial Intelligence workshop series), until the mid-1990s, there was no dedicated archival venue for agentrelated work. By 2000, the situation had changed dramatically; by then, there were two major conferences, a major international workshop, and a dedicated journal, all publishing work in the agents area. Although all these venues were doing good business (there was no shortage of submitted papers), the overheads involved in organizing three major events--not to mention the ...

People often act together with a shared purpose; they collaborate. Collaboration enables them to work more efficiently and to complete activities they could not accomplish individually. An increasing number of computer applications also require collaboration among various systems and people. Thus, a major challenge for AI researchers is to determine how to construct computer systems that are able to act effectively as partners in collaborative activity. Collaborative activity entails participants forming commitments to achieve the goals of the group activity and requires group decision making and group planning procedures.

Agent-based systems technology has generated lots of excitement in recent years because of its promise as a new paradigm for conceptualizing, designing, and implementing software systems. This promise is particularly attractive for creating software that operates in environments that are distributed and open, such as the internet. Currently, the great majority of agent-based systems consist of a single agent. However, as the technology matures and addresses increasingly complex applications, the need for systems that consist of multiple agents that communicate in a peer-topeer fashion is becoming apparent. Central to the design and effective operation of such multiagent systems (MASs) are a core set of issues and research questions that have been studied over the years by the distributed AI community.

Over the past decade, we have been engaged in an extensive research effort to build virtual humans and applications that use them. Building a virtual human might be considered the quintessential AI problem, because it brings together many of the key features, such as autonomy, natural communication, and sophisticated reasoning and behavior, that distinguish AI systems. This article describes major virtual human systems we have built and important lessons we have learned along the way. Early on, we decided to focus on training human-oriented skills, such as leadership, negotiation, and cultural awareness. These skills are based on what is sometimes called tacit knowledge (Sternberg 2000), that is, knowledge that is not easily explicated or taught in a classroom setting but instead is best learned through experience.

The National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (Greenbelt, Md.) has developed the The system is designed to capture and maintain key scientific knowledge while it reduces common errors made by outside scientists. Johnson Controls (Milwaukee, Wis.), a manufacturer of control products used to monitor buildings, has deployed an intelligent agent-based knowledge-retrieval solution at its help desk to provide fast access to support information. Chester, N.Y.) to improve its ability to match reported wage information. The solution will help the agency match contribution information supplied by employers to an employee's Social Security account. RoyScot Trust, the asset finance arm of the Royal Bank of Scotland (Edinburgh, Scotland), has implemented an expert system-based solution to automate the credit-underwriting process.

The Fourth International Conference on Autonomous Agents took place in Barcelona (Catalonia, Spain) from 3 to 7 June 2000, the first one held outside the United States. It had a similar attendance to previous years (435 attendees), thus opening the possibility of other conferences outside the United States. The program committee chairs were Maria Gini, the University of Minnesota, and Jeff Rosenschein, the Hebrew University in Israel. There were 199 submissions from 20 countries, from which 48 papers and 65 posters were selected. Gini managed to obtain some key sponsorship from the Defense Advanced Research Projects Agency (DARPA) and the National Science Foundation to support the travel expenses of more than 25 U.S. students.

IAT'99 was the first meeting in this new series and was held in Hong Kong from 14 to 17 December. It was sponsored by Hong Kong Baptist University, the Croucher Foundation, the Epson Foundation, The MIT Press, the Association for Computing Machinery (ACM) Hong Kong, and the Institute of Electrical and Electronics Engineers Hong Kong Section Computer Chapter and in cooperation with ACM Special Interest Groups in Artificial Intelligence (SIGART), Knowledge Discovery in Data (SIGKDD), and Computer-Human Interaction (SIGCHI). Jiming Liu (Hong Kong Baptist University) and Ning Zhong (Yamaguchi University, Japan) were the program chairs, and Setsuo Ohsuga (Waseda University) and Ernest Lam (Hong Kong Baptist University) were the general chairs. IAT'99 successfully brought together over 150 researchers and practitioners to share their original research results and practical development experiences in intelligent-agent technology. The participants were from Australia, Austria, Belgium, ...

I view the World Wide Web as an information food chain. The maze of pages and hyperlinks that comprise the Web are at the very bottom of the chain. The maze of pages and hyperlinks that comprise the Web are at the very bottom of the chain. Today's Web is populated by a panoply of primitive but popular information services. Is the Web challenge a distraction from our long-term goal of understanding intelligence and building intelligent agents?

To coordinate, intelligent agents might need to know something about themselves, about each other, about how others view themselves and others, about how others think others view themselves and others, and so on. Taken to an extreme, the amount of knowledge an agent might possess to coordinate its interactions with others might outstrip the agent's limited reasoning capacity (its available time, memory, and so on). Much of the work in studying and building multiagent systems has thus been devoted to developing practical techniques for achieving coordination, typically by limiting the knowledge available to, or necessary for, agents. This article categorizes techniques for keeping agents suitably ignorant so that they can practically coordinate and gives a selective survey of examples of these techniques for illustration. Certainly, people who know much (or think they know much) are sometimes subject to cockiness, confusion, paralysis, resignation, or other unpleasant states.