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.

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.

Within computational biology, algorithms are constructed with the aim of extracting knowledge from biological data, in particular, data generated by the large genome projects, where gene and protein sequences are produced in high volume. In this article, we explore new ways of representing protein-sequence information, using machine learning strategies, where the primary goal is the discovery of novel powerful representations for use in AI techniques. In the case of proteins and the 20 different amino acids they typically contain, it is also a secondary goal to discover how the current selection of amino acids -- which now are common in proteins -- might have emerged from simpler selections, or alphabets, in use earlier during the evolution of living organisms.

EUROLAN, which has been held biennially since 1993, is one of the most significant European summer schools in the area of natural language processing. Each of the EUROLAN sessions has focused on an area of timely interest to researchers in the field; this year's EUROLAN involved students in tutorials and hands-on sessions concerned with semantic web technologies as applied to language processing, ontology creation and use, and consideration of the semantic web's potential and limitations.

However, the amount of data this new technology produces is more than one can manually analyze. Hence, the need for automated analysis of microarray data offers an opportunity for machine learning to have a significant impact on biology and medicine. This article describes microarray technology, the data it produces, and the types of machine learning tasks that naturally arise with these data. It also reviews some of the recent prominent applications of machine learning to gene-chip data, points to related tasks where machine learning might have a further impact on biology and medicine, and describes additional types of interesting data that recent advances in biotechnology allow biomedical researchers to collect.

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.

Case-based reasoning (CBR) is a computational reasoning paradigm that involves the storage and retrieval of past experiences to solve novel problems. It is an approach that is particularly relevant in scientific domains, where there is a wealth of data but often a lack of theories or general principles. This article describes several CBR systems that have been developed to carry out planning, analysis, and prediction in the domain of molecular biology.

This science seeks to understand how the complete complement of molecular components of living organisms (nucleic acid, protein, small molecules, and so on) interact together to form living organisms. Functional genomics is of interest to AI because the relationship between machines and living organisms is central to AI and because the field is an instructive and fun domain to apply and sharpen AI tools and ideas, requiring complex knowledge representation, reasoning, learning, and so on. This article describes two machine learning (inductive logic programming [ILP])-based approaches to the bioinformatic problem of predicting protein function from amino acid sequence. The second approach used protein-functional ontologies to provide function classes and a hybrid ILP method to predict function directly from sequence.

This summer's AI conference in Acapulco offered attendees wide variety of program choices as well as ample time to catch up with friends and colleagues. For many, scheduling time was probably the biggest challenge because the conference included numerous invited speakers, 189 technical paper presentations, 93 posters, a Mobile Robot Competition, 19 Innovative Applications of AI (IAAI) award-winning paper presentations, a Trading Agents Competition, a special track on AI and the web, and the vendor exhibit.

Reasoning about spatial data is a key task in many applications, including geographic information systems, meteorological and fluid-flow analysis, computer-aided design, and protein structure databases. Qualitative spatial reasoning (QSR) provides representational primitives (a spatial "vocabulary") and inference mechanisms for these tasks. It then turns to the data-rich case, where the goal is to derive and manipulate qualitative spatial representations that efficiently and correctly abstract important spatial aspects of the underlying data for use in subsequent tasks. This article focuses on how a particular QSR system, SPATIAL AGGREGATION, can help answer spatial queries for scientific and engineering data sets.