Fernando De la Prieta

Agreement technologies and their use in cloud computing environments

Nowadays, cloud computing is revolutionizing the services provided through the Internet to adapt itself in order to keep the quality of its services. Recent research foresees the advent of a new discipline of agent-based cloud computing systems that can make decisions about adaption in an uncertain environment. This paper discusses the role of argumentation in the next generation of agreement technologies and its use in cloud computing environments.

A Multiagent System for Resource Distribution into a Cloud Computing Environment

It is undeniable that the term Cloud Computing has gained in importance at a remarkable pace. It is a technology which is becoming a common element of our life, due to the variety of devices related to the Internet of Things. In this technological frame, there are not many studies in which a Multiagent system has facilitated the management of a cloud-based computational environment; although a first sight its features (autonomy, decentralization, auto-organization, etc.) seem suitable for the task. This study presents the +Cloud which is a cloud platform managed by a Multiagent System.

A model for multi-label classification and ranking of learning objects

Highlights? We use multi-label classification methods for search tagged learning objects (LOs). ? The methodology illustrates the task of multi-label mapping of LOs into types queries. ? We use of multi-label classification algorithm using only the LOs features. ? We also did experiments using web classification with text features. ? Multi-label classifiers such as RAKEL was very effective. This paper describes an approach that uses multi-label classification methods for search tagged learning objects (LOs) by Learning Object Metadata (LOM), specifically the model offers a methodology that illustrates the task of multi-label mapping of LOs into types queries through an emergent multi-label space, and that can improve the first choice of learners or teachers. In order to build the model, the paper also proposes and preliminarily investigates the use of multi-label classification algorithm using only the LO features. As many LOs include textual material that can be indexed, and such indexes can also be used to filter the objects by matching them against user-provided keywords, we then did experiments using web classification with text features to compare the accuracy with the results from metadata (LO feature).

A Multi-agent System that Searches for Learning Objects in Heterogeneous Repositories

This paper presents the BRENHET application, which introduces a new concept in searching for educational resources by using a learning object paradigm that describes these resources. The application is composed of a complete agent-based architecture that implements the concept of federated search. It can search different repositories in parallel, and is based on abstraction layers between the repositories and the search clients.

Methodologies and Intelligent Systems for Technology Enhanced Learning

This volume presents recent research on Methodologies and Intelligent Systems for Technology Enhanced Learning. It contains the contributions of MIS4TEL 2015, which took place in Salamanca, Spain,. On June 3rd to 5th 2015. Like the previous edition, this proceedings and the conference is an open forumfor discussing intelligent systems for Technology Enhanced Learning and empirical methodologies for their design or evaluation MIS4TEL 15 conference has been organized by University of L aquila, Free University of Bozen-Bolzano and the University of Salamanca.

Cloud Computing Integrated into Service-Oriented Multi-Agent Architecture

The main objective of Cloud Computing is to provide software, services and computing infrastructures carried out independently by the network. This concept is based on the development of dynamic, distributed and scalable software. In this way there are Service-Oriented Architectures (SOA) and agent frameworks which provide tools for developing distributed systems and multiagent systems that can be used for the establishment of cloud computing environments. This paper presents CISM@ (Cloud computing Integrated into Service-Oriented Multi-Agent) architecture set on top of the platforms and frameworks by adding new layers for integrating a SOA and Cloud Computing approach and facilitating the distribution and management of functionalities.

SYLPH: An Ambient Intelligence based platform for integrating heterogeneous Wireless Sensor Networks

The significance that Ambient Intelligence (AmI) has acquired in recent years requires the development of innovative solutions. Nonetheless, the development of AmI-based systems requires the creation of increasingly complex and flexible applications. In this regard, the use of context-aware technologies is an essential aspect in these developments to perceive stimuli from the context and react upon it autonomously. This work presents a novel platform that defines a method for integrating dynamic and self-adaptable heterogeneous Wireless Sensor Networks (WSNs). This approach facilitates the inclusion of context-aware capabilities when developing intelligent ubiquitous systems, where functionalities can communicate in a distributed way. Furthermore, the information obtained must be managed by intelligent and self-adaptable technologies to provide an adequate interaction between the users and their environment. Agents and Multi-Agent Systems are one of these technologies. The agents have characteristics such as autonomy, reasoning, reactivity, social abilities and pro-activity which make them appropriate for developing dynamic and distributed systems based on AmI. This way, the integration of the platform with a Service-Oriented Multi-Agent architecture is proposed. Finally, conclusions and future work are presented.

Agents and computer vision for processing stereoscopic images

This paper presents a Multi-Agent System (MAS) that implements techniques of Computer Vision for processing stereoscopic images by using stereo cameras The MAS focuses on detecting people and their behavior through a two-phase method In the first phase, the MAS creates a model of the environment by using a disparity map It can be constructed in real time, even if there are moving objects in the area (such as people passing by) In the second phase, the MAS is able to detect people and their behavior by combining a series of techniques such as Sum of Absolute Differences (SAD) or Gradient Orientation Histograms (HOG) The preliminary results and conclusions after several experiments performed on real scenarios are described in this paper.

Energy Optimization Using a Case-Based Reasoning Strategy

At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices.

Artificial neural networks used in optimization problems

Abstract Optimization problems often require the use of optimization methods that permit the minimization or maximization of certain objective functions. Occasionally, the problems that must be optimized are not linear or polynomial; they cannot be precisely resolved, and they must be approximated. In these cases, it is necessary to apply heuristics, which are able to resolve these kinds of problems. Some algorithms linearize the restrictions and objective functions at a specific point of the space by applying derivatives and partial derivatives for some cases, while in other cases evolutionary algorithms are used to approximate the solution. This work proposes the use of artificial neural networks to approximate the objective function in optimization problems to make it possible to apply other techniques to resolve the problem. The objective function is approximated by a non-linear regression that can be used to resolve an optimization problem. The derivate of the new objective function should be polynomial so that the solution of the optimization problem can be calculated.