Catálogo de publicaciones - libros

The purpose of this paper is to reflect on the nature of human knowledge and that of the knowledge that finally can dwell in an electronic computer. Three frontiers can be distinguished between these two constitutively different types of knowing: (1) The nature of current physical machines (silicon semiconductor crystal) and its organizational restrictions in relation with the biological tissue, which is autonomous, dynamic, tolerant to failures, self-organizative, and adaptive. (2) The semantics of the available algorithms and programming languages in relation with the evolutionary and reactive (behavior-based) biological programming strategies. (3) The nature of current formal tools in relation with natural language.

In this paper we continue along the same line of research started in earlier works, towards to providing a categorical view of structural complexity to optimization problems. The main aim is to provide a universal language for supporting formalisms to specify the hierarchy approximation system for an abstract NP-hard optimization problem. Categorical shape theory provides the mathematical framework to deal with approximation, enabling comparison of objects of interest and of models. In this context, tractable optimization problems are considered as a class of “models” or “prototypes” within a larger class of objects of interest – the intractable optimization problems class. Standard categorial constructions like universal objects, functors and adjunctions allow to formalize an approximation hierarchy system to optimization problems, besides characterizing NP-hard optimization problems as concrete universal objects.

In this paper a way to have structures with partiality in its internal structure in a categorical approach is presented and, with this, a category of partial graphs is given and partial automata are constructed from . With a simple categorical operation, computations of partial automata are given and can be seen as a part of the structure of partial automata.

Boolean algebra provides an important model for the description of knowledge using a binary language. This paper considers a multivalued language, based on arrays and co-arrays, that allows a multivalued description of the knowledge contained in a data table.

This multivalued algebra has some special elements which are arryas and co-arrays at the same time: the degenerate arrays. These degenerate arrays are singled out and their interpretation analyzed, which gives rise to the introduction of the array projections and co-array projections.

Finally, a relation between array projections, co-array projections and uncertain data tables is examined.

The neural network sensitivity analysis, involving neural network training and the calculation of its outputs derivative on inputs, was applied to select the least significant sensor in the multicomponent gas mixtures analysis system. The sensitivity analysis results, collected for various neural network structures were compared with the real significances of the sensors, determined experimentally. The question of the influence of the correlation of the input vector elements on the analysis results was also illustrated and discussed.

This work presents a set of linguistic variables for modelling some geologic and morphological features of a coal seam. Since self-advancing winning coal systems and their interactions with coal faces represent extremely complex situations where traditional mathematical models are unable to offer working solutions, we explore the viability of using Fuzzy-logic based techniques in order to obtain a good-enough model that allows technicians at mine to get a better understanding of the variables at play and to foresee the coal production.

The uncertain variables have been developed as a tool for decision making in a class of uncertain systems described by traditional models or by relational knowledge representations. The purpose of this paper is to show how the uncertain variables may be applied to specific optimization problems formulated for uncertain static plants. A general approach and the optimization with the given certainty threshold are described in the first part. In the second part the application of the presented approach to an optimal distribution problem is considered. Two examples illustrate the presented concepts.

Category Theory is a useful topic to the studies of Computer Science. In spite of experiments with children suggest that categorical reasoning is supposed to be natural to humans, the ones who study Category Theory expose difficulties. This paper starts presenting and analyzing this situation. Then it is developed an evaluation of computational models which represents categorical structures as a way to help students in dealing with categorical concepts intuitively. In the context of this evaluation, a computational model is presented.

When modeling concurrent or parallel systems, we must be aware that basic activities of each system may be constituted by smaller activities, i.e. transitions may be conceptually refined into transactions. Nevertheless, the Unified Modeling Language seems to lack compositional constructs for defining atomic actions/activities/operations. We discuss proper extensions for UML behavioral diagrams that are able to cope with the concept of transaction. Transactions are formally defined through a special morphism between automata in a semantic domain called Nonsequential Automata.

System Dynamics is a discipline for the modeling, simulation and control of complex dynamic systems. In this contribution, the methodology of System Dynamics-based modeling is argued to be a powerful and rigorous approach to theory-building. The strength of the pertinent process of theory development lies in its high standards for model validation, and in a combination of abductive reasoning with induction and deduction. The argument of the paper is underpinned by an application of System Dynamics to the elaboration of a theory in the new field of Cultural Dynamics.