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.

This article discusses a workshop held in conjunction with the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03), held in Acapulco, Mexico, on 11 August 2003. However, much of the work does not take into account real-time constraints typically associated with many agent applications in addition to the incomplete and dynamic nature of the embedding environments. For example, in environments where a number of agents build teams, and both singleagent and collaborative decisions have to be made, such decisions have to be generated rapidly and in the appropriate time windows to be useful. Such topics include world modeling, planning, learning, agent communication, and software architectures. Within this general theme, the aim was to bring together researchers from different communities working with both robots and softbots (for example, RoboCup, cognitive robotics, intelligent autonomous vehicles).

The system consists of an overall agent architecture and five components within the architecture. The five components are (1) goaldirected learning (GDL), a decisiontheoretic method for selecting learning goals; (2) probabilistic bias evaluation (PBE), a technique for using probabilistic background knowledge to select learning biases for the learning goals; (3) uniquely predictive theories (UPTs) and probability computation using independence (PCI), a probabilistic representation and Bayesian inference method for the agent's theories; (4) a probabilistic learning component, consisting of a heuristic search algorithm and a Bayesian method for evaluating proposed theories; and (5) a decision-theoretic probabilistic planner, which searches through the probability space defined by the agent's current theory to select the best action. PAGODA's initial learning goal is just An autonomous agent must be able to select biases (Mitchell 1980) for new learning tasks as they arise. PBE uses probabilistic background knowledge and a model of the system's expected learning performance to compute the expected value of learning biases for each learning goal. The resulting expected discounted future accuracy is used as the expected value of the bias.

This figure shows a small fraction (about 7 km by 8 km) of the entire 75-km-square map. The northern tip of Yellowstone Lake is at the bottom of the screen. Thin black lines represent elevation contours, slightly wider lines represent roads, and the widest lines represent the fireline cut by bulldozers. Blue lines represent rivers and streams. The blue B in the bottom left corner marks the location of the fireboss, the agent that directs all the others.

Indoor mobile robots are becoming reliable enough in navigation tasks to consider working with teams of robots. SHAKEY (remember the STRIPS planner?) In the Office Navigation event, a robot starts from the director's office, determines which of two conference rooms is empty, notifies two professors where and when the meeting will be held, and then returns to tell the director. Points are awarded for accomplishing the different parts of the task, communicating effectively about its goals, and finishing the task quickly. Our strategy was simple: Use as many robots as we could to cut down on the time to find the rooms and notify the professors.

Because of military drawdowns and the need for additional transportation lift requirements, the United States Marine Corps developed a concept that enabled it to modify a commercial container ship to support deployed aviation units. However, a problem soon emerged in that there were too few people who were expert enough to do the unique type of planning required for this ship. Additionally, once someone did develop some expertise, it was time for him/her to move on, retire, or leave active duty. There needed to be a way to capture this knowledge. Access modules are used to access secondand third-tier mobile facilities that are complexed below decks in support of IMA-level repair capability.

In this article, I describe several challenges facing the integration of two distinct lines of AI research: (1) decision-theoretic planning (DTP) and (2) multiagent systems. Both areas (especially the second) are attracting considerable interest, but work in multiagent systems often assumes either classical planning models or prespecified economic valuations on the part of the agents in question. By integrating models of DTP in multiagent systems research, more sophisticated multiagent planning scenarios can be accommodated, at the same time explaining precisely how agents determine their valuations for different sources or activities. I discuss several research challenges that emerge from this integration, involving the development of coordination protocols, the reasoning about lack of coordination, and the predicting of behavior in markets. I also briefly mention some opportunities afforded planning agents in multiagent settings and how these might be addressed.

RoboCup-97, The First Robot World Cup Soccer Games and Conferences, was held at the Fifteenth International Joint Conference on Artificial Intelligence. There were two leagues: (1) real robot and (2) simulation. Ten teams participated in the realrobot league and 29 teams in the simulation league. Over 150 researchers attended the technical workshop. RoboCup-97, the First Robot World Cup Soccer Games and Conferences, was held on 22-28 August 1997 at the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97) (figure 1).