A Scalable Multiagent Platform for Large Systems

[EN] A new generation of open and dynamic systems requires execution frameworks that are capable of being efficient and scalable when large populations of agents are launched. These frameworks must provide efficient support for systems of this kind, by means of an efficient messaging service, agent group management, security issues, etc. To cope with these requirements, in this paper, we present a novel Multiagent Platform that has been developed at the Operating System level. This feature provides high efficiency rates and scalability compared to other high-performance middleware-based Multiagent Platforms.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, and projects TIN2011-27652-C03-01 and TIN2008-04446. Juan M. Alberola has received a grant from Ministerio de Ciencia e Innovacion de Espana (AP2007-00289).Alberola Oltra, JM.; Such Aparicio, JM.; Botti, V.; Espinosa Minguet, AR.; García-Fornes, A. (2013). A Scalable Multiagent Platform for Large Systems. Computer Science and Information Systems. 10(1):51-77. doi:10.2298/CSIS111029039AS517710

Seguridad en sistemas multiagente

La seguridad se está convirtiendo en un aspecto importantísimo dentro de la interoperabilidad entre agentes y Sistemas Multiagente en general, ya que, de forma que las aplicaciones basadas éstos van creciendo, va apareciendo la necesidad de entender los riesgos asociados a su uso. Este trabajo se centra en asegurar la plataforma de agentes Magentix.Such Aparicio, JM. (2008). Seguridad en sistemas multiagente. http://hdl.handle.net/10251/13473Archivo delegad

Smart Distribution Power Systems Reconfiguration using a Novel Multi-agent Approach

The few past years have witnessed a huge leap in the field of the smart grid communication networks in which many theories are being developed, and many applications are being evolved to accommodate the implementation of the smart grid concepts. Distribution power systems are considered to be one of the first leading fields having the strong desire of applying the smart grid concepts; resulting in the emersion of the smart distribution power systems, which are the future visualization of the distribution systems having both the ability of smart acting, and the capabilities of automation, self-healing, and decentralized control. For the sake of the real implementation of the smart distribution power systems, the main functions performed by the traditional systems have to be performed by the new smart systems as well, taking into account the new features and properties of those smart systems. One of those main functions is the ability of power networks optimal reconfiguration to minimize the system’s power loss while preserving the system radial topology. The proposed reconfiguration methodology targets the utilization of a hybrid genetic algorithm with two fuzzy controllers that could converge to the global optimal network configuration with the fastest convergence rate consuming the least computational time. The first fuzzy controller is designed to reject any infeasible system configurations that might show up in the population of the genetic algorithm and violate the system radial topology, while the second fuzzy controller is designed to adapt the mutation rate of the genetic algorithm. Consequently, a novel multi-agent system is proposed and designed to perform the reconfiguration application in smart distribution power systems employing the concepts of distributed processing and decentralized control demanded by those systems. A multi-agent system employs a group of intelligent agents that have the capabilities of autonomy, reactivity, pro-activity, and sociality. Those agents cooperate with each other in order to perform a certain function through their powerful abilities to communicate, socialize, and make a common decision in a decentralized fashion based on the information retrieved from the surrounding environment and compiles with their ultimate objective

An investigation into the issues of multi-agent data mining

Multi-agent systems (MAS) often deal with complex applications that require distributedproblem solving. In many applications the individual and collective behaviourof the agents depends on the observed data from distributed sources. The field of DistributedData Mining (DDM) deals with these challenges in analyzing distributed dataand offers many algorithmic solutions to perform different data analysis and miningoperations in a fundamentally distributed manner that pays careful attention to the resourceconstraints. Since multi-agent systems are often distributed and agents haveproactive and reactive features, combining DM with MAS for data intensive applicationsis therefore appealing.This Chapter discusses a number of research issues concerned with the use ofMulti-Agent Systems for Data Mining (MADM), also known as agent-driven datamining. The Chapter also examines the issues affecting the design and implementationof a generic and extendible agent-based data mining framework. An ExtendibleMulti-Agent Data mining System (EMADS) Framework for integrating distributeddata sources is presented. This framework achieves high-availability and highperformance without compromising the data integrity and security. © 2010 Nova Science Publishers, Inc. All rights reserved