Advances on Practical Applications of Agents and Multiagent Systems

9th International Conference on Practical Applications of Agents and Multiagent Systems, PAAMS 2011, Salamanca,Spain, 6-8 April 2011 (http://www.paams.net/paams2011/) - Proceedings: http://link.springer.com/book/10.1007%2F978-3-642-19875-5International audienceno abstrac

Thomas: Practical Applications of Agents and Multiagent Systems

This paper presents a brief summary of the contents of the special session on practical applications held in the framework of IWANN 2009. The special session has been supported by the THOMAS (TIN2006-14630-C03-03) project and aims at presenting the results obtained in the project, as well as at exchanging experience with other researchers in this field

Neural Systems in Distributed Computing and Artificial Intelligence

This Neurocomputing special issue presents the post-proceedings of the International Conference on Practical Applications on Agents and Multi-Agent Systems (PAAMS 2015) held in Salamanca in June 3th–5th, 2015. PAAMS provides an international forum to present and discuss the latest scientific developments and their effective applications, to assess the impact of the approach, and to facilitate technology transfer. PAAMS started as a local initiative, but has since grown to become the international yearly platform to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development and deployment of Agents and Multi-Agent Systems. PAAMS intends to bring together researchers and developers from industry and the academic world to report on the latest scientific and technical advances on the application of multi-agent systems, to discuss and debate the major issues, and to showcase the latest systems using agent based technology. It will promote a forum for discussion on how agent-based techniques, methods, and tools help system designers to accomplish the mapping between available agent technology and application needs. Other stakeholders should be rewarded with a better understanding of the potential and challenges of the agent-oriented approach

AIDeM: Agent-Based Intrusion Detection Mechanism

The availability of services can be comprimised if a service request sent to the web services server hides some form of attack within its contents. This article presents AIDeM (An Agent-Based Intrusion Detection Mechanism), an adaptive solution for dealing with DoS attacks in Web service environments. The solution proposes a two phased mechanism in which each phase incorporates a special type of CBR-BDI agent that functions as a classifier. In the first phase, a case-based reasoning (CBR) engine utilizes a Naïves Bayes strategy to carry out an initial filter, and in the second phase, a CBR engine incorporates a neural network to complete the classification mechanism. AIDeM has been applied within the FUSION@ architecture to improve its current security mechanism. A prototype of the architecture was developed and applied to a case study. The results obtained are presented in this study

Sistema Multiagente CBR-BDI para el estudio de la interacción mar-aire.

Durante los últimos años se han producido grandes avances en el estudio de la interacción existente entre la atmósfera y la superficie marina. Dicha interacción, y más concretamente el intercambio de CO2, es un factor determinante en el comportamiento de la climatología. En este artículo se presenta un modelo de sistema multiagente que, basándose en la utilización de agentes deliberativos que incorporan sistemas de razonamiento basado en casos, permita modelar y evaluar la interacción mar-aire de forma automática. La arquitectura multiagente propuesta tiene su base en la utilización de una metodología de análisis y diseño que combina elementos de metodologías existentes, como son Gaia y AUML, intentando aprovechar sus ventajas

Multiagent Architecture for Monitoring the North-Atlantic Carbon Dioxide Exchange Rate

This paper presents an architecture that makes it possible to construct dynamic systems capable of growing in dimension and adapting its knowledge to environmental changes. An architecture must define the components of the system (agents in this case), as well as the way in which those components communicate and interact with each other in order to achieve the system’s goals. The work presented here focuses on the development of an agent-based architecture, based on the use of deliberative agents, that incorporate case based reasoning. The proposed architecture requires an analysis and design methodology that facilitates the building of distributed systems using this technology. The proposal combines elements of existing methodologies such as Gaia and AUML in order to take advantage of their characteristics. Moreover the architecture takes into account the possibility of modelling problems in dynamic environments and therefore the use of autonomous models that evolve over time. To solve this problem the architecture incorporates CBR-agents whose aim is to acquire knowledge and adapt themselves to environmental changes. The architecture has been applied to model for evaluating the interaction between the atmosphere and the ocean, as well as for the planification and optimization of sea routes for vessels. The system has been tested successfully, and the results obtained are presented in this paper

Relationship recommender system in a business and employment-oriented social network

[EN] In the last ten years, social networks have had a great influence on people’s lifestyles and have changed, above all, the way users communicate and relate. This is why, one of the main lines of research in the field of social networks focuses on finding and analyzing possible connections between users. These developments allow users to expand on their network of contacts without having to search among the total set of users. However, there are many types of social networks which attract users with specific needs, these needs influence on the type of contacts users are looking for. Our article proposes a relationship recommender system for a business and employment-oriented social network. The presented system functions by extracting relevant information from the social network which it then uses to adequately recommend new contacts and job offers to users. The recommender system uses information gathered from job offer descriptions, user profiles and users’ actions. Then, different metrics are applied in order to discover new ties that are likely to convert into relationships

Hybrid Dynamic Planning Mechanism for Virtual Organizations

It is possible to establish different types of agent organizations according to the type of communication, the coordination among agents, and the type of agents that comprise the group. Each organization 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. This paper presents a new global coordination model for an agent organization. This model is unique in its conception, allowing an organization in a highly dynamic environment to employ self-adaptive capabilities in execution time

A new clustering algorithm applying a hierarchical method neural network.

[EN]Clustering is a branch of multivariate analysis that is used to create groups of data. Most of the existing clustering techniques require defining additional information, including the actual number of clusters, before they can be carried out. This article presents a novel neural network that is capable of creating groups by using a combination of hierarchical clustering and self-organizing maps, without requiring the number of existing clusters to be specified beforehand. The self-organized cluster automatic detection neural network is described in detail, focusing on the density, the average distance, the division algorithm, the update algorithm and the training phase. Three case studies have been carried out in this research in order to evaluate the performance of the neural network, and the results obtained are presented within this article

A CBP agent for monitoring the carbon dioxide exchange rate from satellite images

This work presents a multiagent system for evaluating automatically the interaction that exists between the atmosphere and the ocean surface. Monitoring and evaluating within the Ocean CO2 exchange process is a function requiring working with a great amount of data: satellite images and in-situ vessel’s data. The system presented in this work focuses on Ambient Intelligence (AmI) technologies since the vision of AmI assumes seamless, unobtrusive, and often invisible but also controllable interactions between humans and technology. The work presents the construction of an open multiagent architecture which, based on the use of deliberative agents incorporating case-based planning (CBP) systems, offers a distributed model for such an interaction. This work also presents an analysis and design methodology that facilitates the implementation of CBR agent based distributed artificial intelligent systems. Moreover, the architecture takes into account the fact that the working environment is dynamic and therefore it requires autonomous models that evolve over time. In order to resolve this problem an intelligent environment has been developed, based on the use of CBP-CBR agents, which are capable of handling several goals, constructing plans from the data obtained through satellite images and research vessels, acquiring knowledge and of adapting to environmental changes, are incorporated. The artificial intelligence system has been successfully tested in the North Atlantic Ocean, and the results obtained will be presented within this work