Abstract: Moore’s law has never been so obvious as it is now. New PC’s are equiped with hundreds of Megabytes of main memory, many Gigabytes of secondary storage and processors approaching a Gigaherz clockspeed. Fortunately1 the need for such resources is growing just as fast if not faster.

Abstract: We consider a set of mixed linear and non-linear equations, which arise typically in operative controlling. The variables in the single equations are connected by arithmetic operations. In order to allow for imprecision, each variable is modelled as a fuzzy set with a given membership function. Given a vector of observed variables and an equation system with terms built up by fuzzy sets, a controlling decision is to be made, whether the data set is ‘consistent’ with the equation system or not.

Abstract: Outliers or distorted attributes very often severely interfere with data analysis algorithms that try to extract few meaningful rules. Most methods to deal with outliers try to completely ignore them. This can be potentially harmful since the very outlier that was ignored might have described a rare but still extremely interesting phenomena. In this paper we describe an approach that tries to build an interpretable model while still maintaining all the information in the data. This is achieved through a two stage process. A first phase builds an outlier-model for data points of low relevance, followed by a second stage which uses this model as filter and generates a simpler model, describing only examples with higher relevance, thus representing a more general concept. The outlier-model on the other hand may point out potential areas of interest to the user. Preliminary experiments indicate that the two models in fact have lower complexity and sometimes even offer superior performance.

Abstract: We describe an algorithm for exploratory data analysis which combines adaptive c-means clustering and multi-dimensional scaling (ACMDS). ACMDS is an algorithm for the online visualization of clustering processes and may be considered as an alternative approach to Kohonen’s self organizing feature map (SOM). Whereas SOM is a heuristic neural network algorithm, ACMDS is derived from multivariate statistical algorithms. The implications of ACMDS are illustrated through five different data sets.

Abstract: This paper provides a brief survey of possibilistic logic as a simple and efficient tool for handling nonmonotonic reasoning and data fusion. In nonmonotonic reasoning, Lehmann’s preferential System P is known to provide reasonable but very cautious conclusions, and in particular, preferential inference is blocked by the presence of “irrelevant” properties. When using Lehmann’s rational closure, the inference machinery, which is then more productive, may still remain too cautious. These two types of inference can be represented using a possibility theory-based semantics. The paper proposes several safe ways to overcome the cautiousness of these systems. One of these ways takes advantage of (contextual) independence assumptions of the form: the fact that δ is true (or is false) does not affect the validity of the rule “normally if α then β”. The modelling of such independence assumptions is discussed in the possibilistic framework. This paper presents a general approach for fusing several ordered belief bases provided by different sources according to various modes. More precisely, the paper provides the syntactic counterparts of different ways of aggregating possibility distributions, well-known at the semantic level.

Abstract: Operative Controlling is used in industry and commerce in order to find out whether there exist avoidable irregularities in a given data set or not. These irregularities may be caused by environment, management, and unhonest or erroneous actions of any kind. Controlling is model based in the sense that prior knowledge about facts and structural relationships between variables is used to build up a model of a firm. Its relations are based on definitions, institutional and behavioural equations.

Abstract: Computer Soccer is a testbed for intelligent autonomous machines and programs under real life conditions. Besides others, it provides challenging problems in the design of intelligent agents and in the field of machine learning.