Fernando De La Prieta

The rapid evolution within the context of e-learning is closely linked to international efforts on the standardization of Learning Object (LO), which provides ubiquitous access to multiple and distributed educational resources in many repositories. This article presents a system that enables the recovery and classification of LO and provides individualized help with selecting learning materials to make the most suitable choice among many alternatives. For this classification, it is used a special multi-label data mining designed for the LO ranking tasks. According to each position, the system is responsible for presenting the results to the end user. The learning process is supervised, using two major tasks in supervised learning from multi-label data: multi-label classification and label ranking.

Organizations of agents based on Virtual Organizations needs to be supported by a coordinated effort that explicitly determines how the agents should be organized and carry out the actions and tasks assigned to them. The interactions of a multi-agent system cannot be only related to the agent and the communication skills, but also to the concepts of organizational engineering. Moreover, nowadays there is a clear trend towards using methods and tools that can help to develop and simulate virtual organizations by means of multiagent systems (MAS). Simulation is used for several purposes ranging from work flow to system's procedures representation. The contribution from agent based computing to the field of computer simulation mediated by ABS (Agent Based Simulation) is a new paradigm for the simulation of complex systems that require a high level of interaction between the entities of the system. The main goal of this work is a new global coordination model for an agent organization in a simulation context. The innovation of this work consists of the dynamic and adaptive planning capability to distribute tasks among the agent of the organization. The middleware used for simulation makes it possible to visualize the emergent agent behaviour and the entity agent.

The rapid evolution within the context of e-learning is closely linked to international efforts on the standardization of learning object metadata, which provides learners in a web-based educational system with ubiquitous access to multiple distributed repositories. This article presents a hybrid agent-based architecture that enables the recovery of learning objects tagged in Learning Object Metadata (LOM) and provides individualized help with selecting learning materials to make the most suitable choice among many alternatives.

The rapid evolution within the context of e-learning is closely linked to international efforts on the standardization of learning object metadata, which provides learners in a web-based educational system with ubiquitous access to multiple distributed repositories. This article presents a hybrid agent-based architecture that enables the recovery of learning objects tagged in Learning Object Metadata (LOM) and provides individualized help with selecting learning materials to make the most suitable choice among many alternatives.

Nowadays, the reutilization of learning resources makes possible a reduction of the time of creation educational contents. In this sense, one of the most advances is the use of federated search systems in order to retrieve different resources from a set of repositories more quickly. But nowadays, the main problem is the high heterogeneity in this context. Multiagent Systems witch consider organizational could be the appropriate answer to solve this problem.

This paper proposes the use of federated databases techniques in searching for educational resources by using a learning object paradigm that describes these resources based on metadata. Combining a complete agent-based architecture that implements the concept of federated search along with IR technologies may help organizing and sorting search results in a meaningful way for educational content. The paper presents also the ground for an approach for semantic-aware learning content retrieval based on abstraction layers between the repositories and the search clients.

Advantages of intelligent approaches such as Multi-Agent Systems (MAS) and the use of Wireless Sensor Networks (WSN) within the information fusion process are emerging, especially on context-aware scenarios.However, it is necessary to go further and propose better ways to deal with the avalanche of data that these scenarios provide. In this sense, virtual organizations of agents are an interesting alternative because they provide the necessary capacity to handle open and heterogeneous systems. This paper presents a framework that defines a method for creating virtual organization of software and hardware agents.

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.

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.

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.

Abstract-Wireless Sensor Networks comprise an ideal technology to develop Real-Time Locating Systems (RTLSs) aimed at indoor environments, where existing global navigation satellite systems do not work correctly due to the blockage of the satellite signals. In this ...

Cloud Computing presents a new approach to allow the development of dynamic, distributed and highly scalable software. For this purpose, Cloud Computing offers services, software and computing infrastructure independently through the network. To achieve a system that supports these characteristics, Service-Oriented Architectures (SOA) and agent frameworks exist which provide tools for developing distributed and multi-agent systems that can be used for the establishment of Cloud Computing environments. This paper presents a 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. CISM@ has been applied to the real case study consisting of the analysis of microarray data and has allowed the efficient management of the allocation of resources to the different system agents.

E-healthcare has acquired great importance in recent years and requires the development of innovative solutions. This paper presents a telemonitoring system aimed at enhancing remote healthcare for dependent people at their homes. The system deploys a Service-Oriented Architecture based platform over a heterogeneous Wireless Sensor Networks infrastructure. Furthermore, the information obtained by telemonitoring systems must be managed by intelligent and self-adaptable technologies to provide an adequate interaction between the users and their environment. In the proposed system, the WSNs platform is integrated with a multi-agent architecture so that information gathered by WSN nodes is managed by intelligent agents with reasoning mechanisms.

This paper proposes a multi-agent system (MAS) that uses smart wearable devices and mobile technology for the care of patients in a geriatric home care facility. The use of RFID and NFC technologies allows remote monitoring and identification of patients, and makes it possible for them to receive treatment according to preventative medical protocol. The proposed multi-agent system manages the infrastructure of services within the environment both efficiently and securely by reasoning, task-planning and synchronizing the data obtained from the sensors. A system prototype was installed in a real environment and the results obtained are presented in this paper.

In the current educational context there has been a significant increase in learning object repositories (LOR), which are found in large databases available on the hidden web. All the information about these learning objects (LOs) is described in any metadata labeling standard (LOM, Dublin Core, etc). It is necessary to work and develop solutions that provide efficiency in searching for heterogeneous content and finding distributed context. Distributed information retrieval, or federated search, attempts to respond to the problem of information retrieval in the hidden Web. The main aim of a federated search is to develop models and strategies to get the most benefit from these distributed sources. Thus, users perceive the system as a single point of access to information they require, regardless of the number of sources that exist, its location, or its management mechanism. The process is completely transparent to the user, who does not perceive its complexity and therefore treats any of the information retrieved uniformly. This paper describes an architecture that recovers educational content, called AIREH (Architecture for Intelligent Recovery of Educational content in Heterogeneous Environments), which combines the dynamism and flexibility of multi-agent organizations. The proposal uses systems and mechanisms by which agents acquire roles based on the generation and use of dynamic web services.

One of the most prevalent approaches among distributed architectures is Multi-Agent Systems. The agents have characteristics such as autonomy, reasoning, reactivity, social abilities and pro-activity which make them appropriate for developing distributed systems based on highly dynamic scenarios. Nevertheless, the development of a multi-agent system can be an extensive and delicate process. During this process, it is convenient to utilize Agent-Oriented Software Engineering (AOSE) tools. Such tools facilitate and improve the engineering process, achieving models that are more detailed and closer to the multi-agent systems implementation. This paper presents the analysis and design of a Flexible and User Services Oriented Multi-agent Architecture (FUSION@). This is a new architecture that integrates intelligent agents with a service-oriented approach to facilitate and optimize the development of highly dynamic distributed systems.

This paper presents an improved version of a multiagent architecture aimed at providing solutions for monitoring the interaction between the atmosphere and the ocean. The ocean surface and the atmosphere exchange carbon dioxide. This process is can be modeled by a multiagent system with advanced learning and adaption capabilities. The proposed multiagent architecture incorporates CBR-agents. The CBR-agents proposed in this paper integrate novel strategies that both monitor the parameters that affect the interaction, and facilitate the creation of models. The system was tested and this paper presents the results obtained.

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.

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.