In this article, the author generalizes Dempster`s rule, Yager`s rule, and Dubois-Prade`s rule for belief functions combination in order to be applicable to hyper-power sets according to the Dezert-Smarandache (DSm) Theory. A comparison of the rules with the DSm rule of combination is further presented.

It is shown that learning from data modelled as minimaization of error functionals can be reformulated in terms of inverse problems. This reformulation allows to characterize optimal input-output functions of networks with kernel units.

This paper describes ontological description of computational agents, their properties and abilities. The goal of the work is to allow for autonomous behavior and semi-automatic composition of agents within a multiagent system. The system has to be create foundation for the interchangeability of computational components, and emergence of new models. This paper focuses on ways of representing agents and systems in standard formalisms, such as description logics, OWL, and Jade.

When dealing with image databases, we often need to solve the problem of how to retrieve a desired set of images effectively and efficiently. As a representation of images, there are commonly used some high-dimensional vectors of extracted features, since in such a way the content-based image retrieval is turned into a geometric-search problem. In this article we present a study of feature extraction from raw image data by means of the LSI method (singular-value decomposition, respectively). Simultaneously, we show how such a kind of feature extraction can be used for efficient and effective similarity retrieval using the M-tree index. Because of the application to image retrieval, we also show some interesting effects of LSI, which are not directly obvious in the area of text retrieval (where LSI came from).