173,643 research outputs found

    Creating agent platforms to host agent-mediated services that share resources

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    After a period where the Internet was exclusively filled with content, the present efforts are moving towards services, which handle the raw information to create value from it. Therefore labors to create a wide collection of agent-based services are being perfomed in several projects, such as Agentcities does. In this work we present an architecture for agent platforms named a-Buildings. The aim of the proposed architecture is to ease the creation, installation, search and management of agent-mediated services and the share of resources among services. To do so the a-Buildings architecture creates a new level of abstraction on top of the standard FIPA agent platform specification. Basically, an a-Building is a service-oriented platform which offers a set of low level services to the agents it hosts. We define low level services as those required services that are neccesary to create more complex high level composed services.Postprint (published version

    MonALISA : A Distributed Service System for Monitoring, Control and Global Optimization

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    The MonALISA (Monitoring Agents in A Large Integrated Services Architecture) framework provides a set of distributed services for monitoring, control, management and global optimization for large scale distributed systems. It is based on an ensemble of autonomous, multi-threaded, agent-based subsystems which are registered as dynamic services. They can be automatically discovered and used by other services or clients. The distributed agents can collaborate and cooperate in performing a wide range of management, control and global optimization tasks using real time monitoring information

    Agents mòbils en situacions d'emergència : gestió dinàmica de serveis

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    Aquest projecte consisteix en el disseny i desenvolupament d'una arquitectura de serveis sota el paradigma dels agents inteligents. El propòsit d'ADASMI (Architecture for Dynamic Agent Service Management and Interaction) és permetre la gestió i utilització de serveis per altres agents. L'arquitectura s'ha implementat utilitzant la plataforma d'agents de JADE i es pot utilitzar amb qualsevol altra plataforma que compleixi els estàndards d'IEEE FIPA. A més, és prou flexible com per adaptar-se en entorns dinàmics, com per exemple les xarxes ad-hoc en situacions d'emergència.Este proyecto consiste en el diseño y desarrollo de una arquitectura de servicios bajo el paradigma de los agentes inteligentes. El propósito de ADASMI (Architecture for Dynamic Agent Service Management and Interaction) es permitir la gestión y utilización de servicios por otros agentes. La arquitectura se ha implementado utilizando la plataforma de agentes de JADE y se puede utilizar con cualquier otra plataforma que cumpla los estándares de IEEE FIPA. Además, es bastante flexible como para adaptarse en entornos dinámicos, como por ejemplo las redes ad-hoc en situaciones de emergencia.This project shows the design and development of an agent based services architecture. ADASMI (Architecture for Dynamic Agent Service Management and Interaction) aims at allowing agents the use and management of services. The architecture is implemented using JADE and it is IEEE FIPA-compliant. Moreover, it is very flexible and adapts well to dynamic environments, for example ad-hoc networks in emergency situations

    Adding X-security to Carrel: security for agent-based healthcare applications

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    The high growth of Multi-Agent Systems (MAS) in Open Networks with initiatives such as Agentcities1 requires development in many different areas such as scalable and secure agent platforms, location services, directory services, and systems management. In our case we have focused our effort on security for agent systems. The driving force of this paper is provide a practical vision of how security mechanisms could be introduced for multi-agent applications. Our case study for this experiment is Carrel [9]: an Agent-based application in the Organ and Tissue transplant domain. The selection of this application is due to its characteristics as a real scenario and use of high-risk data for example, a study of the 21 most visited health-related web sites on the Internet discovered that personal information provided at many of the sites was being inadvertently leaked for unauthorized persons. These factors indicate to us that Carrel would be a suitable environment in order to test existing security safeguards. Furthermore, we believe that the experience gathered will be useful for other MAS. In order to achieve our purpose we describe the design, architecture and implementation of security elements on MAS for the Carrel System.Postprint (published version

    An Intelligent Knowledge Management System from a Semantic Perspective

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    Abstract. Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence

    An Intelligent Knowledge Management System from a Semantic Perspective

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    Knowledge Management Systems (KMS) are important tools by which organizations can better use information and, more importantly, manage knowledge. Unlike other strategies, knowledge management (KM) is difficult to define because it encompasses a range of concepts, management tasks, technologies, and organizational practices, all of which come under the umbrella of the information management. Semantic approaches allow easier and more efficient training, maintenance, and support knowledge. Current ICT markets are dominated by relational databases and document-centric information technologies, procedural algorithmic programming paradigms, and stack architecture. A key driver of global economic expansion in the coming decade is the build-out of broadband telecommunications and the deployment of intelligent services bundling. This paper introduces the main characteristics of an Intelligent Knowledge Management System as a multiagent system used in a Learning Control Problem (IKMSLCP), from a semantic perspective. We describe an intelligent KM framework, allowing the observer (a human agent) to learn from experience. This framework makes the system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of stability when performing his domain problem P. To capture by the agent who learn the control knowledge for solving a task-allocation problem, the control expert system uses at any time, an internal fuzzy knowledge model of the (business) process based on the last knowledge model.knowledge management, fuzzy control, semantic technologies, computational intelligence

    Applications of agent architectures to decision support in distributed simulation and training systems

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    This work develops the approach and presents the results of a new model for applying intelligent agents to complex distributed interactive simulation for command and control. In the framework of tactical command, control communications, computers and intelligence (C4I), software agents provide a novel approach for efficient decision support and distributed interactive mission training. An agent-based architecture for decision support is designed, implemented and is applied in a distributed interactive simulation to significantly enhance the command and control training during simulated exercises. The architecture is based on monitoring, evaluation, and advice agents, which cooperate to provide alternatives to the dec ision-maker in a time and resource constrained environment. The architecture is implemented and tested within the context of an AWACS Weapons Director trainer tool. The foundation of the work required a wide range of preliminary research topics to be covered, including real-time systems, resource allocation, agent-based computing, decision support systems, and distributed interactive simulations. The major contribution of our work is the construction of a multi-agent architecture and its application to an operational decision support system for command and control interactive simulation. The architectural design for the multi-agent system was drafted in the first stage of the work. In the next stage rules of engagement, objective and cost functions were determined in the AWACS (Airforce command and control) decision support domain. Finally, the multi-agent architecture was implemented and evaluated inside a distributed interactive simulation test-bed for AWACS Vv\u27Ds. The evaluation process combined individual and team use of the decision support system to improve the performance results of WD trainees. The decision support system is designed and implemented a distributed architecture for performance-oriented management of software agents. The approach provides new agent interaction protocols and utilizes agent performance monitoring and remote synchronization mechanisms. This multi-agent architecture enables direct and indirect agent communication as well as dynamic hierarchical agent coordination. Inter-agent communications use predefined interfaces, protocols, and open channels with specified ontology and semantics. Services can be requested and responses with results received over such communication modes. Both traditional (functional) parameters and nonfunctional (e.g. QoS, deadline, etc.) requirements and captured in service requests

    A Centralized Model for Establishing End-to-End Communication Services via Management Agents

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    This paper presents a centralized approach for establishing end-to-end communication services via management agents. The main proposal is the modular architecture of the third-party based Service Establishment Agent (SEA). The SEA manages inter-provider service negotiation process with per-domain management agents through an appropriate signaling agent. It also receives and interprets end-toend service requests, selects inter-domain paths, performs mapping of service classes among domains on the path, and evaluates conformance of the offered service level with the required one. It allows implementation of different algorithms for the aforementioned functions as well as their selection and combination according to the predefined management policies. Simulation results show that the proposed model significantly outperforms the distributed model in terms of service negotiation times. In the prototype development process, a policy-based solution for mapping of service classes was implemented. The performance evaluation shows that processing requirements for handling multiple service requests are modest, while benefit of the SEA approach is the lack of need to build long-term consensus among providers about technical choices for achieving network interconnection. The SEA architecture is completely independent of the quality of service mechanisms available in particular domains.</p
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