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).

We propose adding to the mix a novel architecture, the process-integrated mechanism (PIM), that enjoys the advantages of having a single controlling authority while avoiding the structural difficulties that have traditionally led to the rejection of centralized approaches in many complex settings. In many situations, PIMs improve on previous models with regard to coordination, security, ease of software development, robustness, and communication overhead. In the PIM architecture, the components are conceived as parts of a single mechanism, even when they are physically separated and operate asynchronously. The PIM model offers promise as an effective infrastructure for handling tasks that require a high degree of time-sensitive coordination between the components, as well as a clean mechanism for coordinating the high-level goals of loosely coupled systems. The PIM model enables coordination without the fragility and high communication overhead of centralized control, but also without the uncertainty associated with the system-level behavior of a multiagent system (MAS).

We examine the design space of auction mechanisms and identify three core activities that structure this space. Formal parameters qualifying the performance of core activities enable precise specification of auction rules. This specification constitutes an auction description language that can be used in the implementation of configurable marketplaces. The specification also provides a framework for organizing previous work and identifying new possibilities in auction design. Given that many multiagent systems involve the allocation of resources, it is natural that the connection between AI and economics has become a common theme in AI. This emphasis is also certainly influenced by the automation of commercial activities on the internet and the potential benefits of intelligent software support for these economic activities. Auctions are central to this confluence of research agendas because they represent a class of basic mechanisms by which economic systems compute the outcome of ...