178,288 research outputs found

    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 MAS-based infrastructure for negotiation and its application to a water-right market

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    The final publication is available at Springer via http://dx.doi.org/10.1007/s10796-013-9443-8This paper presents a MAS-based infrastructure for the specification of a negotiation framework that handles multiple negotiation protocols in a coherent and flexible way. Although it may be used to implement one single type of agreement mechanism, it has been designed in such a way that multiple mechanisms may be available at any given time, to be activated and tailored on demand (on-line) by participating agents. The framework is also generic enough so that new protocols may be easily added. This infrastructure has been successfully used in a case study to implement a simulation tool as a component of a larger framework based on an electronic market of water rights.This paper was partially funded by the Consolider AT project CSD2007-0022 INGENIO 2010 of the Spanish Ministry of Science and Innovation; the MICINN projects TIN2011-27652-C03-01 and TIN2009-13839-C03-01; and the Valencian Prometeo project 2008/051.Alfonso Espinosa, B.; Botti Navarro, VJ.; Garrido Tejero, A.; Giret Boggino, AS. (2014). A MAS-based infrastructure for negotiation and its application to a water-right market. Information Systems Frontiers. 16(2):183-199. https://doi.org/10.1007/s10796-013-9443-8S183199162Alberola, J.M., Such, J.M., Espinosa, A., Botti, V., García-Fornes, A. (2008). Magentix: a multiagent platform integrated in linux. In EUMAS (pp. 1–10).Alfonso, B., Vivancos, E., Botti, V., García-Fornes, A. (2011). Integrating jason in a multi-agent platform with support for interaction protocols. In Proceedings of the compilation of the co-located workshops on AGERE!’11, SPLASH ’11 workshop (pp. 221–226). New York: ACM.Andreu, J., Capilla, J., Sanchis, E. (1996). AQUATOOL, a generalized decision-support system for water-resources planning and operational management. Journal of Hydrology, 177(3–4), 269–291.Bellifemine, F., Caire, G., Greenwood, D. (2007). Developing multi-agent systems with JADE. Wiley.Bordini, R.H., Hübner, J.F., Wooldridge, M. (2007). Programming multi-agent systems in agent speak usign Jason. Wiley.Botti, V., Garrido, A., Gimeno, J.A., Giret, A., Noriega, P. (2011). The role of MAS as a decision support tool in a water-rights market. In AAMAS 2011 workshops, LNAI 7068 (pp. 35–49). Springer.Braubach, L., Pokahr, A., Lamersdorf, W. (2005). Software agent-based applications, platforms and development kits In C.M.K.R. Unland (Ed.), Jadex: a BDI agent system combining middleware and reasoning (Vol. 9, pp. 143–168): Birkhäuser-Verlag.DeSanctis, G.B., & Gallupe, B. (1987). A foundation for the study of group decision support systems. Knowledge based systems, 33(5), 589–609.Eckersley, P. (2003). Virtual markets for virtual goods. Available at http://www.ipria.com/publications/wp/2003/IPRIAWP02.2003.pdf (Accessed April 2012).Fjermestad, J., & Hiltz, S. (2001). Group support systems: a descriptive evaluation of case and field studies. Journal of Management Information Systems, 17(3), 115–161.Fogués, R.L., Alberola, J.M., Such, J.M., Espinosa, A., García-Fornes, A. (2010). Towards dynamic agent interaction support in open multiagent systems. In Proceedings of the 13th international conference of the catalan association for artificial intelligence (Vol. 220, pp. 89–98). IOS Press.Foundation for Intelligent Physical Agents. (2001). FIPA interaction protocol library specification XC00025E. FIPA Consortium.Garrido, A., Arangu, M., Onaindia, E. (2009). A constraint programming formulation for planning: from plan scheduling to plan generatio. Journal of Scheduling, 12(3), 227–256.Giret, A., Garrido, A., Gimeno, J.A., Botti, V., Noriega, P. (2011). A MAS decision support tool for water-right markets. In Proceedings of the tenth international conference on autonomous agents and multiagent systems (Demonstrations@AAMAS) (pp. 1305–1306).Gomez-Limon, J., & Martinez, Y. (2006). Multi-criteria modelling of irrigation water market at basin level: a Spanish case study. European Journal of Operational Research, 173, 313–336.Janjua, N.K., Hussain, F.K., Hussain, O.K. (2013). Semantic information and knowledge integration through argumentative reasoning to support intelligent decision making. Information Systems Frontiers, 15(2), 167–192.jen Hsu, J.Y., Lin, K.-J., Chang, T.-H., ju Ho, C., Huang, H.-S., rong Jih, W. (2006). Parameter learning of personalized trust models in broker-based distributed trust management. Information Systems Frontiers, 8(4), 321–333.Kersten, G., & Lai, H. (2007). European Journal of Operational Research, 180(2), 922–937.Lee, N., Bae, J.K., Koo, C. (2012). A case-based reasoning based multi-agent cognitive map inference mechanism: an application to sales opportunity assessment. Information Systems Frontiers, 14(3), 653–668.Luck, M., & AgentLink. (2005). Agent technology: computing as interaction: a roadmap for agent-based computing. Compiled, written and edited by Michael Luck et al. AgentLink, Southampton.Ma, J., & Orgun, M.A. (2008). Formalizing theories of trust for authentication protocols. Information Systems Frontiers, 10(1), 19–32.Pokahr, A., Braubach, L., Walczak, A., Lamersdorf, W. (2007). Developing multi-agent systems with JADE. Jadex-Engineering Goal-Oriented Agents (pp. 254258). Wiley.Ramos, C., Cordeiro, M., Praça, I., Vale, Z. (2005). Intelligent agents for negotiation and game-based decision support in electricity market. 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    Tool for simulating reputation management algorithms in multiagent systems

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    Efficient service-oriented inter-enterprise collaboration focuses on the business processes of the enterprises and hides the technology differences between them. However, such collaboration induces two main challenges. First, there should be an accessible and functioning infrastructure available for the collaboration. Second, as the growing number of participants can lead to the growing level of misbehaving among them, and thus market deterioration, that is why the parties should have common understanding of the behavior that is appropriate and can be trusted. We focus on the trust relationships between the agents' interactions. Agents make a trust decision before interacting, and this decision among other factors is based on an estimation of another agent's reputation. A reputation management system collects and analyzes interactions experience between agents. Let us call a person, company or any other possible entity whose goal is to build an infrastructure for the interactions between enterprises using one of the trust and reputation management algorithms as an infrastructure builder. For an infrastructure builder it is important to evaluate and compare reputation management systems, choosing one of them based on the evaluation and comparison results. This remains an open question in research. The thesis aims at supporting the decision-making process of this kind. We suggest evaluation criteria for a trust or reputation management systems' evaluation. We implement a generic tool which can plug in a trust or reputation management algorithm and simulate the behavior of the multiagent system where every agent follows the same algorithm. We illustrate the tool's support for some of the suggested evaluation criteria. We provide some recommendations for further development of the generic tool for evaluating and comparing the above-mentioned behavior characteristics of different trust and reputation management algorithms. ACM Computing Classification System (CCS): Human-centered computing → Collaborative and social computing → Collaborative and social computing systems and tools → Reputation systems Computing methodologies → Artificial intelligence → Distributed artificial intelligence → Multi-agent system

    UbiGDSS: a theoretical model to predict decision-makers’ satisfaction

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    The market globalization and the firms’ internationalization hinder the matching of the top managers’ agenda, making it difficult to meet in the same space or time. On the one hand, the appearance of Ubiquitous Group Decision Support Systems (UbiGDSS) enabled individuals to gather and make decisions in different spaces at different times, but on the other hand, originated problems related to the lack of human interaction. To understand how the arguments used can influence each of the decision-makers, what is their satisfaction regarding the decision made, and other affective issues such as emotions and mood, are some examples of that lack. In order to try to overcome this lack, we propose a theoretical model that is specially designed for agents, helping to understand the interactions impact on each agent and their satisfaction with the decision made.This work is supported by “GECAD strategic project”, “EKRUCAMI – EuropeKorea Research on Ubiquitous Computing and Ambient Intelligence” and by National Funds through FCT “Fundação para a Ciência e a Tecnologia” under the projects: PEST-OE/EEI/UI0760/2014, 318878-FP7-PEOPLE and SFRH/BD/89697/2012 respectively.This work is supported by “GECAD strategic project”, “EKRUCAMI – EuropeKorea Research on Ubiquitous Computing and Ambient Intelligence” and by National Funds through FCT “Fundação para a Ciência e a Tecnologia” under the projects: PEST-OE/EEI/UI0760/2014, 318878-FP7-PEOPLE and SFRH/BD/89697/2012 respectively.info:eu-repo/semantics/publishedVersio

    Probabilistic model checking for strategic equilibria-based decision making:advances and challenges

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    Game-theoretic concepts have been extensively studied in economics to provide insight into competitive behaviour and strategic decision making. As computing systems increasingly involve concurrently acting autonomous agents, game-theoretic approaches are becoming widespread in computer science as a faithful modelling abstraction. These techniques can be used to reason about the competitive or collaborative behaviour of multiple rational agents with distinct goals or objectives. This paper provides an overview of recent advances in developing a modelling, verification and strategy synthesis framework for concurrent stochastic games implemented in the probabilistic model checker PRISM-games. This is based on a temporal logic that supports finite- and infinite-horizon temporal properties in both a zero-sum and nonzero-sum setting, the latter using Nash and correlated equilibria with respect to two optimality criteria, social welfare and social fairness. We summarise the key concepts, logics and algorithms and the currently available tool support. Future challenges and recent progress in adapting the framework and algorithmic solutions to continuous environments and neural networks are also outlined

    A State-of-the-art Integrated Transportation Simulation Platform

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    Nowadays, universities and companies have a huge need for simulation and modelling methodologies. In the particular case of traffic and transportation, making physical modifications to the real traffic networks could be highly expensive, dependent on political decisions and could be highly disruptive to the environment. However, while studying a specific domain or problem, analysing a problem through simulation may not be trivial and may need several simulation tools, hence raising interoperability issues. To overcome these problems, we propose an agent-directed transportation simulation platform, through the cloud, by means of services. We intend to use the IEEE standard HLA (High Level Architecture) for simulators interoperability and agents for controlling and coordination. Our motivations are to allow multiresolution analysis of complex domains, to allow experts to collaborate on the analysis of a common problem and to allow co-simulation and synergy of different application domains. This paper will start by presenting some preliminary background concepts to help better understand the scope of this work. After that, the results of a literature review is shown. Finally, the general architecture of a transportation simulation platform is proposed
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