This article gives a comprehensive overview of techniques for personalised hypermedia presentation. It describes the data about the computer user, the computer usage and the physical environment that can be taken into account when adapting hypermedia pages to the needs of the current user. Methods for acquiring these data, for representing them as models in formal systems and for making generalisations and predictions about the user based thereon are discussed. Different types of hypermedia adaptation to the individual user's needs are distinguished and recommendations for further research and applications given. While the focus of the article is on hypermedia adaptation for improving customer relationship management utilising the World Wide Web, many of the techniques and distinctions also apply to other types of personalised hypermedia applications within and outside the World Wide Web, like adaptive educational systems.

Many solutions to AI problems require the task to be represented in one of a multitude of rigorous mathematical formalisms. The construction of such mathematical models forms a difficult problem which is often left to the user of the problem-solver. This void between problem-solvers and their problems is studied by the eclectic field of automated modelling. Within this field, compositional modelling, a knowledge-based methodology for system-modelling, has established itself as a leading approach. In general, a compositional modeller organises knowledge in a structure of composable fragments that relate to particular system components or processes. Its embedded inference mechanism chooses the appropriate fragments with respect to a given problem, instantiates and assembles them into a consistent system model. Many different types of compositional modeller exist, however, with significant differences in their knowledge representation and approach to inference. This paper examines compositional modelling. It presents a general framework for building and analysing compositional modellers. Based on this framework, a number of influential compositional modellers are examined and compared. The paper also identifies the strengths and weaknesses of compositional modelling and discusses some typical applications.

The first International Workshop on Chance Discovery (CD) was held in the hot-springs resort of Matsue, Shimani Prefecture, Japan, on 22 May 2001, as part of the Fifteenth Annual Conference of the Japanese Society for Artificial Intelligence (JSAI-2001). Thirteen presentations were made to the workshop (Ohsawa, 2001), with 25 people attending. The majority of presenters and attendees were from Japan. An edited selection of the papers presented will be included in a volume of papers from the various International Workshops of JSAI-2001, to be published in the Springer series, Advanced Information Processing. A forthcoming special issue of the journal New Generation Computing will also address this topic.