Browsing by Autor "Jose Aguilar"

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Many-Objective Optimization Approach using Surrogate Models in Rotational Cattle Grazing
(2025) Marvin Jiménez Narváez; Jose Aguilar; Juan Manuel Montoya; Edwin Montoya
In the context of many-objective problems, one of the most important problems is to be able to efficiently model and evaluate the different objectives of the problem. A strategy to speed up the computation, as well as to manage the uncertainties due to partial knowledge of the context, is to use surrogate models. The objective of this paper is to evaluate the hybrid use of surrogate models in the context of a many-objective optimization problem for livestock management. In particular, we propose to use hybrid objective functions, in which parts of the objective function originally constructed analytically are replaced by data-driven surrogate models. Specifically, we explore several hybrid schemes, where in each we combine different parts of the analytical objective function with other data-driven parts. Preliminary results show that the hybrid optimization models studied possess a competitive performance quality against the original purely analytical model, becoming an interesting proposal to manage the computational times and uncertainty of environments such as livestock farming. These results show the great potential of using models built with machine learning techniques to replace analytically constructed objective functions that are not always able to absorb the non-deterministic nature of livestock grazing, and represent an opportunity to further explore their usefulness in this context.
MODELO INTELIGENTE PARA BASES DE DATOS DISTRIBUIDAS
(2011) Ana C. Muñoz G.; Jose Aguilar; Rodrigo Martínez
RESUMENEn este trabajo trataremos el problema del diseño del “Modelo Inteligente para Sistemas de Bases de Datos Distribuidas”. Particularmente, nos proponemos diseñar el modelo canónico a través del manejo ontológico de la información. Para esto se diseñan ontologías que permitirán describir una base de datos como un conjunto de términos representacionales de sus diferentes componentes. En estas ontologías, las definiciones asocian clases, relaciones, funciones, entre otras cosas, de entidades en el universo del discurso de las bases de datos, para describir el significado de las bases de datos, sus componentes, restricciones, etc. La razón de usar ontologías es que ellas definen conceptos y relaciones dentro de un marco taxonómico, cuya conceptualización está representada de una manera formal, legible y utilizable. En trabajos anteriores [14] se ha propuesto un modelo de referencia y una arquitectura para la integración de Bases de Datos en donde se plantea la necesidad de definir un modelo canónico. Como continuación de estos trabajos, en este artículo se describen las taxonomías ontológicas que componen el modelo de referencia para la integración de bases datos, y se diseña el Modelo Canónico usando dicha noción ontológica. De esta manera, se define el proceso de integración entre los diferentes tipos de bases de datos, estas bases de datos componentes pueden ser: Relacionales, Orientadas a Objeto, Difusas, Inteligentes y Multimedia. Así, el esquema ontológico describe los conceptos, operaciones y restricciones, tanto de las bases de datos componentes como de su proceso de integración.Además en este trabajo se muestra también los axiomas para cada una de los esquemas ontológicos utilizando lógica de predicado de primer orden.PALABRAS CLAVESEsquema ontológicoModelo Canónico de DatosBases de Datos Distribuidas InteligentesIntegración de Bases de DatosABSTRACTIn this abstract we will analyze-look at with the problem of the design of the "Intelligent Model for Distributed Database System".We particularly set out to design the canonical model through the ontological handling of the information. To do so,ontology is designed that allow the description of a database like a set of representative terms of their different components. In this ontology, the definitions associate classes, relations, functions, among other things, of organizations in the speech universe of the data bases, to describe their meaning, its components, restrictions, etc. The reason for using ontology is that it defines concepts and relations within a taxonomic frame, whose conceptualization is represented in a formal, legible and usable way. In previous works [14] a reference model and architecture for the integration of database the need to define an intelligent canonical model was proposed. Like continuation of these works, in this article the ontological taxonomies are described, determining the component of the model of reference for the integration of database, and the Canonical Model is designed using this ontological notion. By doing so, the process of integration between the different types of database is defined. These component data bases can be: Relational, OO, Fuzzy, Intelligent and Multimedia. Thus, the ontological scheme describes the concepts, operations and restrictions, as well as, the component database and its process of integration. In this work there are also the axioms for each one of the ontological schemes using first-order predicate logic.KEYWORDSOntological SchemeCanonical data ModelDistributed Database IntelligentDatabase Integration
Verification of the emergence in an architecture for multi-robot systems (AMEB)
(European Organization for Nuclear Research, 2018) Ángel Gil; Jose Aguilar; Eladio Dapena; Rafael Rivas
This article analyzes the emerging behavior of a multi-robot system managed by an architecture structured in three layers: the first provides local support to the robot, manages its processes of action, perception and communication, as well as its behavioral aspect, which considers the reactive, cognitive and social aspects of the robot. In addition, it introduces an affective component that influences its behavior and the way it relates to the environment and to the other individuals in the system, based on an emotional model that takes into account fourArticle history:Received 12 September 2018Accepted 08 November 2018A Gil, pertenece al Laboratorio de Prototipos en la Universidad Nacional Experimental del Táchira y a Tepuy R+D Group. Artificial Intelligence Software Development. Mérida, Venezuela (email: agil@unet.edu.ve)basic emotions. The second provides support to the collective processes of the system, based on the concept of emerging coordination. The latter is responsible for knowledge management and learning processes, both individually and collectively, in the system. In this article the metrics are defined to verify the emergency in the system, by means of the use of a method of verification of emergent behaviors based on Fuzzy Cognitive Maps.