This paper describes two versions of a novel approach to developing binary classifiers, based on two evolutionary computation paradigms: cellular programming and genetic programming. Such an approach achieves high computation efficiency both during evolution and at runtime. Evolution speed is optimized by allowing multiple solutions to be computed in parallel. Runtime performance is optimized explicitly using parallel computation in the case of cellular programming or implicitly taking advantage of the intrinsic parallelism of bitwise operators on standard sequential architectures in the case of genetic programming. The approach was tested on a digit recognition problem and compared with a reference classifier.

The authors present a model, called HDPTN (hierarchical and distributed process transition network), for the representation of real world phenomena. This model is based on two types of entities: circumstances and episodes. A circumstance is an elementary entity which can take on a set of discrete values over time, while an episode is a compound entity whose parts are a circumstance and/or other episodes acting concurrently. In particular, the hierarchical and distributed structure of the episode seems to be suitable for the modeling of systems which need real-time performance and the integration of representations and reasoning at different levels of detail.>

When a scene is described by means of natural language sentences, many details are usually omitted, because they are not in the focus of the conversation. Moreover, natural language is not the best tool to define precisely positions and spatial relationships. The process of interpreting ambiguous statements and inferring missing details involves many types of knowledge, from linguistics to physics. This paper is mainly concerned with the problem of modeling the process of understanding descriptions of static scenes. The specific topics covered by this work are the analysis of the meaning of spatial prepositions, the problem of the reference system and dimensionality, the activation of expectations about unmentioned objects, the role of default knowledge about object positions and its integration with contextual information sources, and the problem of space representation. The issue of understanding dynamic scenes descriptions is briefly approached in the last section.

In this paper the experience made through the development of a NAtural Language driven Image Generation is discussed. This system is able to imagine a static scene described by means of a sequence of simple phrases. In particular, a theory for equilibrium and support will be outlined together with the problem of object positioning.