The rate at which expert annotators add the experimental information into more or less controlled vocabularies of databases snails along at an even slower pace. Most methods that annotate protein function exploit sequence similarity by transferring experimental information for homologues. A crucial development aiding such transfer is large-scale, work- and management-intensive projects aimed at developing a comprehensive ontology for gene-protein function, such as the Gene Ontology project. Some of these tools target parsing controlled vocabulary from databases; others venture at mining free texts from MEDLINE abstracts or full scientific papers.

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.

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.

Complex electro-mechanical products, such as high-end printers and photocopiers, are designed as families, with reusable modules put together in different manufacturable configurations, and the ability to add new modules in the field. The modules are controlled locally by software that must take into account the entire configuration. This poses two problems for the manufacturer. This has become an accepted part of the practice of Xerox, and the control software is deployed in high-end Xerox printers.

A wide range of sensor-rich, networked embedded systems are being created that must operate robustly for years in the face of novel failures by managing complex autonomic processes. Our objective is to revolutionize the way in which we control these new artifacts by creating reactive model-based programming languages that enable everyday systems to reason intelligently and enable machines to explore other worlds. The program's executive automatically coordinates system interactions to achieve these states, entertaining known and potential failures, using models of its constituents and environment. Model-based programming is being generalized to hybrid discrete-continuous systems and the coordination of networks of robotic vehicles.

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.

Traditional approaches to ecological modeling, based on mathematical equations, are hampered by the qualitative nature of ecological knowledge. In this article, we demonstrate that qualitative reasoning provides alternative and productive ways for ecologists to develop, organize, and implement models. We present a qualitative theory of population dynamics and use this theory to capture and simulate commonsense theories about population and community ecology. Advantages of this approach include the possibility of deriving relevant conclusions about ecological systems without numeric data; a compositional approach that enables the reusability of models representing partial behavior; the use of a rich vocabulary describing objects, situations, relations, and mechanisms of change; and the capability to provide causal interpretations of system behavior.

In general, modeling is a complex and creative task, and building qualitative models is no exception. In this article, we review approaches to learning qualitative models, either from numeric data or qualitative observations. We illustrate this using applications associated with systems control, in particular, the identification and optimization of controllers and human operator's control skill. We also review approaches that learn models in terms of qualitative differential equations.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2003 Spring Symposium Series, Monday through Wednesday, 24-26 March 2003, at Stanford University. The titles of the eight symposia were Agent-Mediated Knowledge Management, Computational Synthesis: From Basic Building Blocks to High- Level Functions, Foundations and Applications of Spatiotemporal Reasoning (FASTR), Human Interaction with Autonomous Systems in Complex Environments, Intelligent Multimedia Knowledge Management, Logical Formalization of Commonsense Reasoning, Natural Language Generation in Spoken and Written Dialogue, and New Directions in Question-Answering Motivation.