Ohio State University) focused on the use of case-based reasoning for both planning and world modeling. Nicola Muscettola (NASA Ames) focused on reactive behaviors. Laboratory) described an approach Within this general theme, to planning with multiagent the aim was to bring together researchers execution. The presentation ecent developments in multiagent shown promising results in the robotics, intelligent autonomous of Thomas Wagner (University of modeling of autonomous, collaborative vehicles). The common denominator Brement), Christoph Schlieder (University behavior between agents in different that these groups share is the pragmatic of Bamberg), and Ubbo Visser environments.

"This book will appeal to technophiles, interdisciplinarians, and broad thinkers of all

To build a machine that has "common sense" was once a principal goal in the field of artificial intelligence. But most researchers in recent years have retreated from that ambitious aim. Instead, each developed some special technique that could deal with some class of problem well, but does poorly at almost everything else. We are convinced, however, that no one such method will ever turn out to be "best," and that instead, the powerful AI systems of the future will use a diverse array of resources that, together, will deal with a great range of problems. To build a machine that's resourceful enough to have humanlike common sense, we must develop ways to combine the advantages of multiple methods to represent knowledge, multiple ways to make inferences, and multiple ways to learn. We held a two-day symposium in St. Thomas, U.S. Virgin Islands, to discuss such a project -- - to develop new architectural schemes that can bridge between different strategies and representations. This article reports on the events and ideas developed at this meeting and subsequent thoughts by the authors on how to make progress.

Incremental search reuses information from previous searches to find solutions to a series of similar search problems potentially faster than is possible by solving each search problem from scratch. This is important because many AI systems have to adapt their plans continuously to changes in (their knowledge of) the world. In this article, we give an overview of incremental search, focusing on LIFELONG PLANNING A*, and outline some of its possible applications in AI.

(ICKEDS 2004). This book looks at some of the results of the synergy among AI, cognitive science, and education. Examples include virtual students whose misconceptions force students to reflect on their own knowledge, intelligent tutoring systems, and speech-recognition technology that helps students learn to read. Some of the systems described are already used in classrooms and have been evaluated; a few are still laboratory efforts. The book also addresses cultural and political issues involved in the deployment of new educational technologies.

INTF-1 technical search team as it arrived on site and conducted a reconnaissance of the collapsed building. The scientists also got to observe the process by which the search team manager decided whether to use traditional tools, such as acoustic sensors or search cameras, or a robot (figure 2). The robots were deployed the National Science Foundation's Each scientist went into with rescue workers as they went the rubble at least two times and witnessed through a complete deploy-searchcleanup the deployment of each brand Search and Rescue (CRASAR) at cycle or "evolution." Embedding under such realistic conditions permitted the participants to gain an ethnographic understanding of rescue robotics, direct access to one type of collapse site, and an introduction to standard operating procedures such as decontaminating the robots that might impact the design of better robots and software. The scientists brought sleeping bags and slept during the single four-hour rest cycle allotted to the rescue workers.

The 2003International Conference on Automated Planning and Scheduling (ICAPS-03) was held 9 to 13 June 2003 in Trento, Italy. It was chaired by Enrico Giunchiglia (University of Genova), Nicola Muscettola (NASA Ames), and Dana Nau (University of Maryland). Piergiorgio Bertoli and Marco Benedetti (both from ITC-IRST) were the local chair and the workshop-tutorial coordination chair, respectively.

This article reports on the RoboCup-2003 event. RoboCup is no longer just the Soccer World Cup for autonomous robots but has evolved to become a coordinated initiative encompassing four different robotics events: (1) Soccer, (2) Rescue, (3) Junior (focused on education), and (4) a Scientific Symposium. RoboCup-2003 took place from 2 to 11 July 2003 in Padua (Italy); it was colocated with other scientific events in the field of AI and robotics. In this article, in addition to reporting on the results of the games, we highlight the robotics and AI technologies exploited by the teams in the different leagues and describe the most meaningful scientific contributions.

A novel algorithm for actively trading stocks is presented. While traditional expert advice and ``universal'' algorithms (as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirical results on historical markets provide strong evidence that this type of technical trading can ``beat the market'' and moreover, can beat the best stock in the market. In doing so we utilize a new idea for smoothing critical parameters in the context of expert learning.

With the recent technological feasibility of electronic commerce over the Internet, much attention has been given to the design of electronic markets for various types of electronically-tradable goods. Such markets, however, will normally need to function in some relationship with markets for other related goods, usually those downstream or upstream in the supply chain. Thus, for example, an electronic market for rubber tires for trucks will likely need to be strongly influenced by the rubber market as well as by the truck market. In this paper we design protocols for exchange of information between a sequence of markets along a single supply chain. These protocols allow each of these markets to function separately, while the information exchanged ensures efficient global behavior across the supply chain. Each market that forms a link in the supply chain operates as a double auction, where the bids on one side of the double auction come from bidders in the corresponding segment of the industry, and the bids on the other side are synthetically generated by the protocol to express the combined information from all other links in the chain. The double auctions in each of the markets can be of several types, and we study several variants of incentive compatible double auctions, comparing them in terms of their efficiency and of the market revenue.