350 research outputs found

    Reorganization in Dynamic Agent Societies

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    En la nueva era de tecnologías de la información, los sistemas tienden a ser cada vez más dinámicos, compuestos por entidades heterogéneas capaces de entrar y salir del sistema, interaccionar entre ellas, y adaptarse a las necesidades del entorno. Los sistemas multiagente han contribuído en los ultimos años, a modelar, diseñar e implementar sistemas autónomos con capacidad de interacción y comunicación. Estos sistemas se han modelado principalmente, a través de sociedades de agentes, las cuales facilitan la interación, organización y cooperación de agentes heterogéneos para conseguir diferentes objetivos. Para que estos paradigmas puedan ser utilizados para el desarrollo de nuevas generaciones de sistemas, características como dinamicidad y capacidad de reorganización deben estar incorporadas en el modelado, gestión y ejecución de estas sociedades de agentes. Concretamente, la reorganización en sociedades de agentes ofrece un paradigma para diseñar aplicaciones abiertas, dinámicas y adaptativas. Este proceso requiere determinar las consecuencias de cambiar el sistema, no sólo en términos de los beneficios conseguidos sinó además, midiendo los costes de adaptación así como el impacto que estos cambios tienen en todos los componentes del sistema. Las propuestas actuales de reorganización, básicamente abordan este proceso como respuestas de la sociedad cuando ocurre un cambio, o bien como un mecanismo para mejorar la utilidad del sistema. Sin embargo, no se pueden definir procesos complejos de decisión que obtengan la mejor configuración de los componentes organizacionales en cada momento, basándose en una evaluación de los beneficios que se podrían obtener así como de los costes asociados al proceso. Teniendo en cuenta este objetivo, esta tesis explora el área de reorganización en sociedades de agentes y se centra principalmente, en una propuesta novedosa para reorganización. Nuestra propuesta ofrece un soporte de toma de decisiones que considera cambios en múltiplesAlberola Oltra, JM. (2013). Reorganization in Dynamic Agent Societies [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19243Palanci

    Exploiting the Use of Cooperation in Self-Organizing Reliable Multiagent Systems

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    In this paper, a novel and cooperative approach is exploited introducing a self-organizing engine to achieve high reliability and availability in multiagent systems. The Adaptive Multiagent Systems theory is applied to design adaptive groups of agents in order to build reliable multiagent systems. According to this theory, adaptiveness is achieved via the cooperative behaviors of agents and their ability to change the communication links autonomously. In this approach, there is not a centralized control mechanism in the multiagent system and there is no need of global knowledge of the system to achieve reliability. This approach was implemented to demonstrate its performance gain in a set of experiments performed under different operating conditions. The experimental results illustrate the effectiveness of this approach

    OperA/ALIVE/OperettA

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    Comprehensive models for organizations must, on the one hand, be able to specify global goals and requirements but, on the other hand, cannot assume that particular actors will always act according to the needs and expectations of the system design. Concepts as organizational rules (Zambonelli 2002), norms and institutions (Dignum and Dignum 2001; Esteva et al. 2002), and social structures (Parunak and Odell 2002) arise from the idea that the effective engineering of organizations needs high-level, actor-independent concepts and abstractions that explicitly define the organization in which agents live (Zambonelli 2002).Peer ReviewedPostprint (author's final draft

    TRAMMAS: Enhancing Communication in Multiagent Systems

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    Tesis por compendio[EN] Over the last years, multiagent systems have been proven to be a powerful and versatile paradigm, with a big potential when it comes to solving complex problems in dynamic and distributed environments, due to their flexible and adaptive behavior. This potential does not only come from the individual features of agents (such as autonomy, reactivity or reasoning power), but also to their capability to communicate, cooperate and coordinate in order to fulfill their goals. In fact, it is this social behavior what makes multiagent systems so powerful, much more than the individual capabilities of agents. The social behavior of multiagent systems is usually developed by means of high level abstractions, protocols and languages, which normally rely on (or at least, benefit from) agents being able to communicate and interact indirectly. However, in the development process, such high level concepts habitually become weakly supported, with mechanisms such as traditional messaging, massive broadcasting, blackboard systems or ad hoc solutions. This lack of an appropriate way to support indirect communication in actual multiagent systems compromises their potential. This PhD thesis proposes the use of event tracing as a flexible, effective and efficient support for indirect interaction and communication in multiagent systems. The main contribution of this thesis is TRAMMAS, a generic, abstract model for event tracing support in multiagent systems. The model allows all entities in the system to share their information as trace events, so that any other entity which require this information is able to receive it. Along with the model, the thesis also presents an abstract architecture, which redefines the model in terms of a set of tracing facilities that can be then easily incorporated to an actual multiagent platform. This architecture follows a service-oriented approach, so that the tracing facilities are provided in the same way than other traditional services offered by the platform. In this way, event tracing can be considered as an additional information provider for entities in the multiagent system, and as such, it can be integrated from the earliest stages of the development process.[ES] A lo largo de los últimos años, los sistemas multiagente han demostrado ser un paradigma potente y versátil, con un gran potencial a la hora de resolver problemas complejos en entornos dinámicos y distribuidos, gracias a su comportamiento flexible y adaptativo. Este potencial no es debido únicamente a las características individuales de los agentes (como son su autonomía, y su capacidades de reacción y de razonamiento), sino que también se debe a su capacidad de comunicación y cooperación a la hora de conseguir sus objetivos. De hecho, por encima de la capacidad individual de los agentes, es este comportamiento social el que dota de potencial a los sistemas multiagente. El comportamiento social de los sistemas multiagente suele desarrollarse empleando abstracciones, protocolos y lenguajes de alto nivel, los cuales, a su vez, se basan normalmente en la capacidad para comunicarse e interactuar de manera indirecta de los agentes (o como mínimo, se benefician en gran medida de dicha capacidad). Sin embargo, en el proceso de desarrollo software, estos conceptos de alto nivel son soportados habitualmente de manera débil, mediante mecanismos como la mensajería tradicional, la difusión masiva, o el uso de pizarras, o mediante soluciones totalmente ad hoc. Esta carencia de un soporte genérico y apropiado para la comunicación indirecta en los sistemas multiagente reales compromete su potencial. Esta tesis doctoral propone el uso del trazado de eventos como un soporte flexible, efectivo y eficiente para la comunicación indirecta en sistemas multiagente. La principal contribución de esta tesis es TRAMMAS, un modelo genérico y abstracto para dar soporte al trazado de eventos en sistemas multiagente. El modelo permite a cualquier entidad del sistema compartir su información en forma de eventos de traza, de tal manera que cualquier otra entidad que requiera esta información sea capaz de recibirla. Junto con el modelo, la tesis también presenta una arquitectura {abs}{trac}{ta}, que redefine el modelo como un conjunto de funcionalidades que pueden ser fácilmente incorporadas a una plataforma multiagente real. Esta arquitectura sigue un enfoque orientado a servicios, de modo que las funcionalidades de traza son ofrecidas por parte de la plataforma de manera similar a los servicios tradicionales. De esta forma, el trazado de eventos puede ser considerado como una fuente adicional de información para las entidades del sistema multiagente y, como tal, puede integrarse en el proceso de desarrollo software desde sus primeras etapas.[CA] Al llarg dels últims anys, els sistemes multiagent han demostrat ser un paradigma potent i versàtil, amb un gran potencial a l'hora de resoldre problemes complexes a entorns dinàmics i distribuïts, gràcies al seu comportament flexible i adaptatiu. Aquest potencial no és només degut a les característiques individuals dels agents (com són la seua autonomia, i les capacitats de reacció i raonament), sinó també a la seua capacitat de comunicació i cooperació a l'hora d'aconseguir els seus objectius. De fet, per damunt de la capacitat individual dels agents, es aquest comportament social el que dóna potencial als sistemes multiagent. El comportament social dels sistemes multiagent solen desenvolupar-se utilitzant abstraccions, protocols i llenguatges d'alt nivell, els quals, al seu torn, es basen normalment a la capacitat dels agents de comunicar-se i interactuar de manera indirecta (o com a mínim, es beneficien en gran mesura d'aquesta capacitat). Tanmateix, al procés de desenvolupament software, aquests conceptes d'alt nivell son suportats habitualment d'una manera dèbil, mitjançant mecanismes com la missatgeria tradicional, la difusió massiva o l'ús de pissarres, o mitjançant solucions totalment ad hoc. Aquesta carència d'un suport genèric i apropiat per a la comunicació indirecta als sistemes multiagent reals compromet el seu potencial. Aquesta tesi doctoral proposa l'ús del traçat d'esdeveniments com un suport flexible, efectiu i eficient per a la comunicació indirecta a sistemes multiagent. La principal contribució d'aquesta tesi és TRAMMAS, un model genèric i abstracte per a donar suport al traçat d'esdeveniments a sistemes multiagent. El model permet a qualsevol entitat del sistema compartir la seua informació amb la forma d'esdeveniments de traça, de tal forma que qualsevol altra entitat que necessite aquesta informació siga capaç de rebre-la. Junt amb el model, la tesi també presenta una arquitectura abstracta, que redefineix el model com un conjunt de funcionalitats que poden ser fàcilment incorporades a una plataforma multiagent real. Aquesta arquitectura segueix un enfoc orientat a serveis, de manera que les funcionalitats de traça són oferides per part de la plataforma de manera similar als serveis tradicionals. D'aquesta manera, el traçat d'esdeveniments pot ser considerat com una font addicional d'informació per a les entitats del sistema multiagent, i com a tal, pot integrar-se al procés de desenvolupament software des de les seues primeres etapes.Búrdalo Rapa, LA. (2016). TRAMMAS: Enhancing Communication in Multiagent Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61765TESISCompendi

    Multi-dimensional adaptation in MAS organizations

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    © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Organization adaptation requires determining the consequences of applying changes not only in terms of the benefits provided but also measuring the adaptation costs as well as the impact that these changes have on all of the components of the organization. In this paper, we provide an approach for adaptation in multiagent systems based on a multidimensional transition deliberation mechanism (MTDM). This approach considers transitions in multiple dimensions and is aimed at obtaining the adaptation with the highest potential for improvement in utility based on the costs of adaptation. The approach provides an accurate measurement of the impact of the adaptation since it determines the organization that is to be transitioned to as well as the changes required to carry out this transition. We show an example of adaptation in a service provider network environment in order to demonstrate that the measurement of the adaptation consequences taken by the MTDM improves the organization performance more than the other approaches.Manuscript received January 2, 2012; revised July 26, 2012; accepted August 7, 2012. Date of publication August 31, 2012; date of current version April 16, 2013. This work was supported in part by projects TIN2008-04446 and TIN2009-13839-C03-01. J. M. Alberola received a Grant from Ministerio de Ciencia e Innovacion de Espana (AP2007-00289). This paper was recommended by Associate Editor J. Huang.Alberola Oltra, JM.; Julian Inglada, VJ.; García-Fornes, A. (2013). Multi-dimensional adaptation in MAS organizations. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 43(2):622-633. https://doi.org/10.1109/TSMCB.2012.2213592S62263343

    Integrating driving forces into the development of Adaptive Virtual Organizations

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    Organizations have become the backbone of the society. Humans live around all kinds of organizations, such as neighborhood communities, businesses, schools, unions, political, sports, and religious organizations, etc. These organizations have a set of members, each playing a specific role, which determines their duties and functionalities within the organization. One of these functionalities is to offer a range of services to members of the organization and external people. These members must follow a set of norms to ensure the proper functioning of the organization and should pursue the global goals of the organization. A feature that is repeated in organizations is that they are not static but dynamic, resulting in changes in both its structure and the way in which they behave. In an organization, any of its elements is prone to change due to situations that occur in the organization itself or its environment. Researchers in the field of social sciences and organizations have studied such situations, the reasons why they appear and solutions and actions to be taken to ensure that this situation does not damage the organization or to take advantage of the situation. These situations are known as ‘Forces that drive organizational change’. Human organizations are the main source of inspiration for the Multi-Agent Systems (MAS) based on organizations. These systems are computational abstractions that are populated by agents instead of people, but take into account organizational elements such as roles, services, goals, norms, etc. However, the proposals that have been presented up to now to define this type of MAS are focused mostly on static systems, without changes in its structure. Moreover, in the few proposals that take into account organizational changes, they just state that changes occur, but without specifying the reason for change. Thus, the concept of ‘forces that drive organizational change’ (and their features) is not considered. Therefore, the objective of this PhD thesis is to translate the knowledge of the forces that drive organizational change available in human organizations to MASbased organizations. These forces will be formally expressed with the factors that help to detect them. The solutions to be taken when a force is detected will also be presented. To correctly perform this task, a formalization for virtual organizations is designed, named Virtual Organization Formalization (VOF). Moreover, the Artifacts for Organizational Mechanisms are proposed, which are a tool to help in the representation of organizational knowledge and in the modeling of the environment of the organization. This tool is based on the Agents & Artifacts (A&A) framework.Esparcia García, S. (2015). Integrating driving forces into the development of Adaptive Virtual Organizations [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48538TESI

    Utility-Based Mechanism for Structural Self-Organization in Service-Oriented MAS

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    Structural relations established among agents influence the performance of decentralized service discovery process in multiagent systems. Moreover, distributed systems should be able to adapt their structural relations to changes in environmental conditions. In this article, we present a service-oriented multiagent systems, where agents initially self-organize their structural relations based on the similarity of their services. During the service discovery process, agents integrate a mechanism that facilitates the self-organization of their structural relations to adapt the structure of the system to the service demand. This mechanism facilitates the task of decentralized service discovery and improves its performance. Each agent has local knowledge about its direct neighbors and the queries received during discovery processes. With this information, an agent is able to analyze its structural relations and decide when it is more appropriate to modify its direct neighbors and select the most suitable acquaintances to replace them. The experimental evaluation shows how this self-organization mechanism improves the overall performance of the service discovery process in the system when the service demand changesThis work is partially supported by the Spanish Ministry of Science and Innovation through grants CSD2007-0022 (CONSOLIDER-INGENIO 2010), TIN2012-36586-C03-01, TIN2012-36586-C03-01, TIN2012-36586-C03-02, PROMETEOII/2013/019, and FPU grant AP-2008-00601 awarded to E. Del Val.Del Val Noguera, E.; Rebollo Pedruelo, M.; Vasirani, M.; Fernández, A. (2014). Utility-Based Mechanism for Structural Self-Organization in Service-Oriented MAS. ACM Transactions on Autonomous and Adaptive Systems. 9(3):1-24. https://doi.org/10.1145/2651423S12493Sherief Abdallah and Victor Lesser. 2007. Multiagent reinforcement learning and self-organization in a network of agents. In Proceedings of the 6th International Conference on Autonomous Agents and Multiagent Systems. 172--179.Lada A. Adamic and Bernardo A. Huberman. 2002. Zipf’s law and the Internet. Glottometrics 3, 143--150.Muntasir Al-Asfoor, Brendan Neville, and Maria Fasli. 2012. Heuristic resource search in a self-organised distributed multi agent system. In Proceedings of the 6th International Workshop on Self-Organizing Systems. 84--89.Mathieu Aquin, Salman Elahi, and Enrico Motta. 2010. Personal monitoring of Web information exchange: Towards Web lifelogging. In Proceedings of the Web Science Conference.Ulrich Basters and Matthias Klusch. 2006. RS2D: Fast adaptive search for semantic Web services in unstructured p2p networks. In Proceedings of the International Semantic Web Conference. 87--100.Umesh Bellur and Roshan Kulkarni. 2007. Improved matchmaking algorithm for semantic Web services based on bipartite graph matching. In Proceedings of the International Semantic Web Conference. 86--93.Devis Bianchini, Valeria De Antonellis, and Michele Melchiori. 2009. Service-based semantic search in p2p systems. In Proceedings of the European Conference on Web Services. 7--16.Bartosz Biskupski, Jim Dowling, and Jan Sacha. 2007. Properties and mechanisms of self-organizing MANET and P2P systems. ACM Transactions on Autonomous and Adaptive Systems 2, 1, 1--34.Alberto Blanc, Yi-Kai Liu, and Amin Vahdat. 2005. Designing incentives for peer-to-peer routing. In Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies. 374--385.Michael Bowling and Manuela Veloso. 2002. Multiagent learning using a variable learning rate. Artificial Intelligence 136, 215--250.Frances M. T. Brazier, Jeffrey O. Kephart, H. Van Dyke Parunak, and Michael N. Huhns. 2009. Agents and service-oriented computing for autonomic computing: A research agenda. IEEE Internet Computing 13, 3, 82--87.Tyson Condie, Sepandar D. Kamvar, and Hector Garcia-Molina. 2004. Adaptive peer-to-peer topologies. In Proceedings of the 4th International Conference on Peer-to-Peer Computing. 53--62.Arturo Crespo and Hector Garcia-Molina. 2002. Routing indices for peer-to-peer systems. In Proceedings of the 22nd International Conference on Distributed Computing Systems. 23--32.Elena Del Val, Natalia Criado, Carlos Carrascosa, Vicente Julian, Miguel Rebollo, Estefania Argente, and Vicente Botti. 2010. THOMAS: A service-oriented framework for virtual organizations. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS’10). 1631--1632.Elena Del Val, Miguel Rebollo, and Vicente Botti. 2011. Introducing homophily to improve semantic service search in a self-adaptive system. In Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems. 1241--1242.Elena Del Val, Miguel Rebollo, and Vicente Botti. 2012a. Enhancing decentralized service discovery in open service-oriented multi-agent systems. Autonomous Agents and Multi-Agent Systems 28, 1, 1--30.Elena Del Val, Miguel Rebollo, and Vicente Botti. 2012b. Promoting cooperation in service-oriented MAS through social plasticity and incentives. Journal of Systems and Software 86, 2, 520--537.Gianni Di Caro, Frederick Ducatelle, and Luca Maria Gambardella. 2005. AntHocNet: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks. European Transactions on Telecommunications 16, 443--455.Ding Ding, Lei Liu, and Hartmut Schmeck. 2010. Service discovery in self-organizing service-oriented environments. In Proceedings of the 2010 IEEE Asia-Pacific Services Computing Conference. 717--724.Sergey N. Dorogovtsev and Jose F. F. Mendes. 2003. Evolution of Networks: From Biological Nets to the Internet and WWW. Oxford University Press.Giovanna Di Marzo Serugendo, Marie-Pierre Gleizes, and Anthony Karageorgos. 2011. Self-Organizing Software: From Natural to Artificial Adaptation. Natural Computing Series.Erik Einhorn and Andreas Mitschele-Thiel. 2008. RLTE: Reinforcement learning for traffic-engineering. In Proceedings of the 2nd International Conference on Autonomous Infrastructure, Management, and Security. 120--133.Nelson Fernandez, Carlos Maldonado, and Carlos Gershenson. 2014. Information measures of complexity, emergence, self-organization, homeostasis, and autopoiesis. In Guided Self-Organization: Inception. Emergence, Complexity and Computation, Vol. 9. Springer, 19--51. DOI: http://dx.doi.org/10.1007/978-3-642-53734-9_2Jose Luis Fernandez-Marquez, Josep Lluis Arcos, and Giovanna Di Marzo Serugendo. 2012. A decentralized approach for detecting dynamically changing diffuse event sources in noisy WSN environments. Applied Artificial Intelligence 26, 4, 376--397. DOI: http://dx.doi.org/10.1080/08839514.2012.653659Agostino Forestiero, Carlo Mastroianni, and Michela Meo. 2009. Self-Chord: A bio-inspired algorithm for structured P2P systems. In Proceedings of the 9th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing. 44--51.Matthew E. Gaston and Marie des Jardins. 2005. Agent-organized networks for multi-agent production and exchange. In Proceedings of the 20th AAAI Conference on Artificial Intelligence. 77--82.Nathan Griffiths and Michael Luck. 2010. Changing neighbours: Improving tag-based cooperation. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems. 249--256.Peter Haase, Ronny Siebes, and Frank van Harmelen. 2008. Expertise-based peer selection in peer-to-peer networks. Knowledge and Information Systems 15, 1, 75--107.Philip N. Howard, Lee Rainee, and Steve Jones. 2001. Days and nights on the Internet. American Behavioural Scientist, 383--404.Bernardo A. Huberman and Lada A. Adamic. 2000. The nature of markets in the WWW. Quarterly Journal of Electronic Commerce 1, 5--12.Michael N. Huhns et al. 2005. Research directions for service-oriented multiagent systems. IEEE Internet Computing 9, 6, 65--70.Tomoko Itao, Tatsuya Suda, Tetsuya Nakamura, Miyuki Imada, Masato Matsuo, and Tomonori Aoyama. 2001. Jack-in-the-Net: Adaptive networking architecture for service emergence. In Proceedings of the Asian-Pacific Conference on Communications. 9.Emily M. Jin, Michelle Girvan, and Mark E. J. Newman. 2001. Structure of growing social networks. Physical Review E 64, 4, 046132.Sachin Kamboj and Keith S. Decker. 2007. Organizational self-design in semi-dynamic environments. In Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems. 335--337.Rahamatullah Khondoker, S. M. Taslim Arif, Nathan Kerr, and Dennis Schwerdel. 2011. Self-organizing communication services in future network architectures. In Proceedings of the 5th International Workshop on Self-Organizing Systems.Matthias Klusch, Benedikt Fries, and Katia Sycara. 2009. OWLS-MX: A hybrid Semantic Web service matchmaker for OWL-S services. Web Semantics Science Services and Agents on the World Wide Web 7, 2, 121--133.Dionisis Kontominas, Paraskevi Raftopoulou, Christos Tryfonopoulos, and Euripides G. M. Petrakis. 2013. DS4: A distributed social and semantic search system. Advances in Information Retrieval 7814, 832--836.Ramachandra Kota, Nicholas Gibbins, and Nicholas R. Jennings. 2012. Decentralized approaches for self-adaptation in agent organizations. ACM Transactions on Autonomous and Adaptive Systems 7, 1, Article No. 1.Paul Lazarsfeld. 1954. Friendship as a social process: A substantive and methodological analysis. In Freedom and Control in Modern Society. Van Nostrand, New York, NY.Paulo Leito. 2013. Towards self-organized service-oriented multi-agent systems. In Service Orientation in Holonic and Multi Agent Manufacturing and Robotics. Studies in Computational Intelligence, Vol. 472. Springer, 41--56.W. Sabrina Lin, Hong Vikcy Zhao, and K. J. Ray Liu. 2009. Incentive cooperation strategies for peer-to-peer live multimedia streaming social networks. IEEE Transactions on Multimedia 11, 3, 396--412.Sheila A. McIlraith, Tran Cao Son, and Honglei Zeng. 2001. Semantic Web services. IEEE Intelligent Systems 16, 2, 46--53.Miller McPherson, Lynn Smith-Lovin, and James Cook. 2001. Birds of a feather: Homophily in social networks. Annual Review of Sociology 27, 415--444.Vivek Nallur and Rami Bahsoon. 2012. A decentralized self-adaptation mechanism for service-based applications in the cloud. IEEE Transactions on Software Engineering 99, 591--612.Aris Ouksel, Yair Babad, and Thomas Tesch. 2004. Matchmaking software agents in B2B markets. In Proceedings of the 37th Annual Hawaii International Conference on System Sciences. 1--9.Massimo Paolucci, Takahiro Kawamura, Terry R. Payne, and Katia P. Sycara. 2002. Semantic matching of Web services capabilities. In Proceedings of the 1st International Semantic Web Conference. 333--347.Leonid Peshkin and Virginia Savova. 2002. Reinforcement learning for adaptive routing. In Proceedings of the 2002 International Conference on Neural Networks (IJCNN’02). 1825--1830.Paraskevi Raftopoulou and Euripides G. M. Petrakis. 2008. iCluster: A self-organizing overlay network for P2P information retrieval. In Proceedings of the 30th European Conference on Advances in Information Retrieval (ECIR’08). 65--76.Sharmila Savarimuthu, Maryam Purvis, Martin Purvis, and Bastin Tony Roy Savarimuthu. 2011. Mechanisms for the self-organization of peer groups in agent societies. In Multi-Agent-Based Simulation XI. Lecture Notes in Computer Science, Vol. 6532. Springer, 93--107.Giovanna Di Marzo Serugendo, Marie-Pierre Gleizes, and Anthony Karageorgos. 2005. Self-organization in multi-agent systems. Knowledge Engineering Review 20, 2, 165--189.Abdul Khalique Shaikh, Saadat M. Alhashmi, and Rajendran Parthiban. 2012. A semantic impact in decentralized resource discovery mechanism for grid computing environments. In Algorithms and Architectures for Parallel Processing. Lecture Notes in Computer Science, Vol. 7440. Springer, 206--216.Qixiang Sun and Hector Garcia-Molina. 2004. SLIC: A selfish link-based incentive mechanism for unstructured peer-to-peer networks. In Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS’04). 506--515.Mirko Viroli and Franco Zambonelli. 2010. A biochemical approach to adaptive service ecosystems. Information Sciences 180, 10, 1876--1892. DOI: http://dx.doi.org/10.1016/j.ins.2009.11.021Li Wang. 2011. SoFA: An expert-driven, self-organization peer-to-peer semantic communities for network resource management. Expert Systems with Applications 38, 1, 94--105.Kevin Werbach. 2000. Syndication—the emerging model for business in the Internet era. Harvard Business Review 78, 3, 84--93, 214.Tom Wolf and Tom Holvoet. 2005. Emergence versus self-organisation: Different concepts but promising when combined. In Engineering Self-Organising Systems. Lecture Notes in Computer Science, Vol. 3464. Springer, 1--15.Haizheng Zhang, W. Bruce Croft, Brian Levine, and Victor Lesser. 2004. A multi-agent approach for peer-to-peer based information retrieval system. In Proceedings of the 3rd International Conference on Autonomous Agents and Multiagent Systems, Vol. 1. 456--463.Ming Zhong. 2006. Popularity-biased random walks for peer-to-peer search under the square-root principle. In Proceedings of the 5th International Workshop on Peer-to-Peer Systems

    Agent Based Control of Electric Power Systems with Distributed Generation

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    Towards next generation coordination infrastructures

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    Coordination infrastructures play a central role in the engineering of multiagent systems. Since the advent of agent technology, research on coordination infrastructures has produced a significant number of infrastructures with varying features. In this paper, we review the the state-of-the-art coordination infrastructures with the purpose of identifying open research challenges that next generation coordination infrastructures should address. Our analysis concludes that next generation coordination infrastructures must address a number of challenges: (i) to become socially aware, by facilitating human interaction within a MAS; (ii) to assist agents in their decision making by providing decision support that helps them reduce the scope of reasoning and facilitates the achievement of their goals; and (iii) to increase openness to support on-line, fully decentralised design and execution. Furthermore, we identify some promising approaches in the literature, together with the research issues worth investigating, to cope with such challenges. © Cambridge University Press, 2015.The work presented in this paper has been partially funded by projects EVE (TIN2009-14702-C02-01), AT (CSD2007-0022), and the Generalitat of Catalunya grant 2009-SGR-1434Peer Reviewe

    Combination of self-organization mechanisms to enhance service discovery in open systems

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    Decentralized systems have emerged as an alternative to centralized approaches for dealing with dynamic requirements in new business models. These systems should provide mechanisms that contribute to flexibility and facilitate adaptation to changes in the environment. In this paper, we present two self-organization mechanisms for a decentralized service discovery system in order to improve its performance. These mechanisms are based on local actions of agents that only consider local information about queries they forward during the discovery process. The self-organization actions are chosen by each agent individually when the agent considers them to be appropriate. The actions are: remaining in the system, leaving the system, cloning, and changing structural relations with other agents. We have evaluated each self-organization mechanism separately but also the combination of the two as the environmental conditions in the service demand change. The results show that the proposed self-organization mechanisms considerably improve the performance of the service discovery systemDel Val Noguera, E.; Rebollo Pedruelo, M.; Botti Navarro, VJ. (2014). Combination of self-organization mechanisms to enhance service discovery in open systems. Information Sciences. 279:138-162. doi:10.1016/j.ins.2014.03.109S13816227
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