This work extends the semantics and implementation of fuzzy prolog presented in [VGM02] in order to include Default Knowledge capability. The new semantic allows non-uniform default assumptions and has Closed World Assumption (CWA) and Open World Assumption (OWA) as particular…

The Knowledge Engineering Suite is an ontology production method, based on relationship networks, for knowledge representation inside specific contexts. The production of these ontologies has three basic steps, since catching the client data, knowledge base creation, and information retrieval and…

It is interesting and important to compare, analyze and assess the alternative tools that could be used in the area of Knowledge Representation. In this paper we present a reasearch line associated to this goal: to formally establish the relation…

This paper proposes an original generic hierarchical framework in order to facilitate the modeling stage of complex autonomous robotics mission planning problems with action uncertainties. Such stochastic planning problems can be modeled as Markov Decision Processes [5]. This work is…

Given an instance of an optimization problem together with an optimal solution, we consider the scenario in which this instance is modified locally. In graph problems, e. g., a singular edge might be removed or added, or an edge weight…

This article presents a procedure for constructing a taxonomy of COTS products in the field of Requirements Engineering (RE). The taxonomy and the obtained information reach transcendental benefits to the selection of systems and tools that aid to RE-related actors…

Effective document retrieval using domain knowledge and semantics is one of the major challenges in Information Retrieval. Over the last years, there has been a growing interest in ontologies as an artifact for human knowledge representation and a critical component…

This paper presents a neuro-fuzzy network where all its parameters can be tuned simultaneously using Genetic Algorithms. The approach combines the merits of fuzzy logic theory, neural networks and genetic algorithms. The proposed neuro-fuzzy network does not require a priori…

Complex control tasks may be solved by dividing them into a more specific and more easily handled subtasks hierarchy. Several authors have demonstrated that the incremental layered evolution paradigm allows obtaining controllers capable of solving this type of tasks. In…

This work is focussed on the development and analysis of a new class of algorithms, called cellular memetic algorithms (cMAs), which will be evaluated here on the satisfiability problem (SAT). For describing a cMA, we study the effects of adding…