This book constitutes the refereed proceedings of the 5th International Conference on Discovery Science, DS 2002, held in Lubeck, Germany, in November 2002.The 17 revised full papers and 27 revised short papers presented together with 5 invited contributions were carefully reviewed and selected from 76 submissions. The papers are organized in topical sections on applications of discovery science to natural science, knowledge discovery from unstructured and semi-structured data, metalearning and analysis of machine learning algorithms, combining machine learning algorithms, neural networks and statistical learning, new approaches to knowledge discovery, and knowledge discovery from text.

This volume contains all the papers presented at the Ninth International Con- rence on Algorithmic Learning Theory (ALT'98), held at the European education centre Europ]aisches Bildungszentrum (ebz) Otzenhausen, Germany, October 8{ 10, 1998. The Conference was sponsored by the Japanese Society for Arti cial Intelligence (JSAI) and the University of Kaiserslautern. Thirty-four papers on all aspects of algorithmic learning theory and related areas were submitted, all electronically. Twenty-six papers were accepted by the program committee based on originality, quality, and relevance to the theory of machine learning. Additionally, three invited talks presented by Akira Maruoka of Tohoku University, Arun Sharma of the University of New South Wales, and Stefan Wrobel from GMD, respectively, were featured at the conference. We would like to express our sincere gratitude to our invited speakers for sharing with us their insights on new and exciting developments in their areas of research. This conference is the ninth in a series of annual meetings established in 1990. The ALT series focuses on all areas related to algorithmic learning theory including (but not limited to): the theory of machine learning, the design and analysis of learning algorithms, computational logic of/for machine discovery, inductive inference of recursive functions and recursively enumerable languages, learning via queries, learning by arti cial and biological neural networks, pattern recognition, learning by analogy, statistical learning, Bayesian/MDL estimation, inductive logic programming, robotics, application of learning to databases, and gene analyses."

In the contemporary city, the physical infrastructure and sensorial experiences of two millennia are now inter-woven within an invisible digital matrix. This matrix alters human perceptions of the city, informs our behaviour and increasingly influences the urban designs we ultimately inhabit. Digital Futures and the City of Today cuts through these issues to analyse the work of architects, designers, media specialists and a growing number of community activists, laying out a multi-faceted view of the complex integrated phenomenon of the contemporary city. Split into three sections, the book interrogates the concept of the 'smart' city, examines innovative digital projects from around the world, documents experimental visions for the future, and describes projects that engage local communities in the design process.