16,118 research outputs found

    A Generic Agent Organisation Framework For Autonomic Systems

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    Autonomic computing is being advocated as a tool for managing large, complex computing systems. Specifically, self-organisation provides a suitable approach for developing such autonomic systems by incorporating self-management and adaptation properties into large-scale distributed systems. To aid in this development, this paper details a generic problem-solving agent organisation framework that can act as a modelling and simulation platform for autonomic systems. Our framework describes a set of service-providing agents accomplishing tasks through social interactions in dynamically changing organisations. We particularly focus on the organisational structure as it can be used as the basis for the design, development and evaluation of generic algorithms for self-organisation and other approaches towards autonomic systems

    Agent Based Modeling and Simulation: An Informatics Perspective

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    The term computer simulation is related to the usage of a computational model in order to improve the understanding of a system's behavior and/or to evaluate strategies for its operation, in explanatory or predictive schemes. There are cases in which practical or ethical reasons make it impossible to realize direct observations: in these cases, the possibility of realizing 'in-machina' experiments may represent the only way to study, analyze and evaluate models of those realities. Different situations and systems are characterized by the presence of autonomous entities whose local behaviors (actions and interactions) determine the evolution of the overall system; agent-based models are particularly suited to support the definition of models of such systems, but also to support the design and implementation of simulators. Agent-Based models and Multi-Agent Systems (MAS) have been adopted to simulate very different kinds of complex systems, from the simulation of socio-economic systems to the elaboration of scenarios for logistics optimization, from biological systems to urban planning. This paper discusses the specific aspects of this approach to modeling and simulation from the perspective of Informatics, describing the typical elements of an agent-based simulation model and the relevant research.Multi-Agent Systems, Agent-Based Modeling and Simulation

    Trust Strategies for the Semantic Web

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    Everyone agrees on the importance of enabling trust on the SemanticWebto ensure more efficient agent interaction. Current research on trust seems to focus on developing computational models, semantic representations, inference techniques, etc. However, little attention has been given to the plausible trust strategies or tactics that an agent can follow when interacting with other agents on the Semantic Web. In this paper we identify five most common strategies of trust and discuss their envisaged costs and benefits. The aim is to provide some guidelines to help system developers appreciate the risks and gains involved with each trust strategy

    Trust on the Web: Some Web Science Research Challenges

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    Web Science is the interdisciplinary study of the World Wide Web as a first-order object in order to understand its relationship with the wider societies in which it is embedded, and in order to facilitate its future engineering as a beneficial object. In this paper, research issues and challenges relating to the vital topic of trust are reviewed, showing how the Web Science agenda requires trust to be addressed, and how addressing the challenges requires a range of disciplinary skills applied in an integrated manner

    Trajectories in Physical Space out of Communications in Acquaintance Space: An Agent-Based Model of a Textile Industrial District

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    This article presents an agent-based model of an Italian textile district where thousands of small firms specialize in particular phases of fabrics production. It is an empirical and methodological model that reconstructs the communications between firms when they arrange production chains. In their turn, production chains reflect into road traffic in the geographical areas where the district extends. The reconstructed traffic exhibits a pattern that has been observed, but not foreseen, by policy makers

    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

    How Can Social Networks Ever Become Complex? Modelling the Emergence of Complex Networks from Local Social Exchanges

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    Small-world and power-law network structures have been prominently proposed as models of large networks. However, the assumptions of these models usually lack sociological grounding. We present a computational model grounded in social exchange theory. Agents search attractive exchange partners in a diverse population. Agent use simple decision heuristics, based on imperfect, local information. Computer simulations show that the topological structure of the emergent social network depends heavily upon two sets of conditions, harshness of the exchange game and learning capacities of the agents. Further analysis show that a combination of these conditions affects whether star-like, small-world or power-law structures emerge.Complex Networks, Power-Law, Scale-Free, Small-World, Agent-Based Modeling, Social Exchange Theory, Structural Emergence

    'Women's agency and the fallacy of autonomy: the example of rape and sexual consent'

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    This edited collection explores the agency of women who do violence and have violence done to them. Topics covered include rape, pornography, prostitution, suicide bombing and domestic violence. The volume contributes to the philosophical and theoretical debate, as well as offering practical, social and political responses to the issues examined

    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
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