TRAMMAS: Enhancing Communication in Multiagent Systems

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

The Information Flow Problem in multi-agent systems

[EN] One of the problems related to the multi-agent systems area is the adequate exchange of information within the system. This problem is not only related to the availability of highly efficient and sophisticated message-passing mechanisms, which are in fact provided with by current multi-agent platforms, but also to the election of an appropriate communication strategy, which may also greatly influence the ability of the system to cope with the exchange of large amounts of data. Ideally, the communication strategy should be compatible with how the information flows in the system, that is, how agents share their knowledge with each other in order to fulfill the system-level goals. In this way, MAS designers must deal with the problem of analyzing the multi-agent system with respect the communication strategy that best suits the way the information flows in that particular system. This paper presents a formalization of this problem, which has been coined as the Information Flow Problem, and also presents a complete case study with an empirical evaluation involving four well-known communication strategies and eight typical multi-agent systems.This work was partially supported by MINECO/FEDER TIN2015-65515-C4-1-R and TIN2014-55206-R of the Spanish government.Búrdalo Rapa, LA.; Terrasa Barrena, AM.; Julian Inglada, VJ.; García-Fornes, A. (2018). The Information Flow Problem in multi-agent systems. Engineering Applications of Artificial Intelligence. 70:130-141. https://doi.org/10.1016/j.engappai.2018.01.011S1301417

Analyzing the effect of gain time on soft task scheduling policies in real-time systems

In hard real-time systems, gain time is defined as the difference between the Worst Case Execution Time (WCET) of a hard task and its actual processor consumption at runtime. This paper presents the results of an empirical study about how the presence of a significant amount of gain time in a hard real-time system questions the advantages of using the most representative scheduling algorithms or policies for aperiodic or soft tasks in fixed-priority preemptive systems. The work presented here refines and complements many other studies in this research area in which such policies have been introduced and compared. This work has been performed by using the authors' testing framework for soft scheduling policies, which produces actual, synthetic, randomly generated applications, executes them in an instrumented Real-Time Operating System (RTOS), and finally processes this information to obtain several statistical outcomes. The results show that, in general, the presence of a significant amount of gain time reduces the performance benefit of the scheduling policies under study when compared to serving the soft tasks in background, which is considered the theoretical worst case. In some cases, this performance benefit is so small that the use of a specific scheduling policy for soft tasks is questionable. © 2012 IEEE.This work is partially funded by research projects PROMETEO/2008/051, CSD2007-022, and TIN2008-04446.Búrdalo Rapa, LA.; Terrasa Barrena, AM.; Espinosa Minguet, AR.; García Fornes, AM. (2012). Analyzing the effect of gain time on soft task scheduling policies in real-time systems. IEEE Transactions on Software Engineering. 38(6):1305-1318. https://doi.org/10.1109/TSE.2011.95S1305131838

Analyzing the effect of gain time on soft task scheduling policies in real-time systems

Búrdalo Rapa, LA. (2011). Analyzing the effect of gain time on soft task scheduling policies in real-time systems. http://hdl.handle.net/10251/11396.Archivo delegad

TRAMMAS: A tracing model for multiagent systems

Agents flexibility and autonomy, as well as their capacity to coordinate and cooperate, are some of the features which make multiagent systems useful to work in dynamic and distributed environments. These key features are directly related to the way in which agents communicate and perceive each other, as well as their environment and surrounding conditions. Traditionally, this has been accomplished by means of message exchange or by using blackboard systems. These traditional methods have the advantages of being easy to implement and well supported by multiagent platforms; however, their main disadvantage is that the amount of social knowledge in the system directly depends on every agent actively informing of what it is doing, thinking, perceiving, etc. There are domains, for example those where social knowledge depends on highly distributed pieces of data provided by many different agents, in which such traditional methods can produce a great deal of overhead, hence reducing the scalability, efficiency and flexibility of the multiagent system. This work proposes the use of event tracing in multiagent systems, as an indirect interaction and coordination mechanism to improve the amount and quality of the information that agents can perceive from both their physical and social environment, in order to fulfill their goals more efficiently. In order to do so, this work presents an abstract model of a tracing system and an architectural design of such model, which can be incorporated to a typical multiagent platform.This work is partially supported by projects PROMETEO/2008/051, CS02007-022, TIN2008-04446 and TIN2009-13839-C03-01.Búrdalo Rapa, LA.; Terrasa Barrena, AM.; Julian Inglada, VJ.; García-Fornes, A. (2011). TRAMMAS: A tracing model for multiagent systems. Engineering Applications of Artificial Intelligence. 24(7):1110-1119. https://doi.org/10.1016/j.engappai.2011.06.010S1110111924

An adaptive framework for monitoring agent organizations

The final publication is available at Springer via http://dx.doi.org/10.1007/s10796-013-9478-xMultiagent technologies are usually considered to be suitable for constructing agent organizations that are capable of running in dynamic and distributed environments and that are able to adapt to changes as the system runs. The necessary condition for this adaptation ability is to make agents aware of significant changes in both the environment and the organization. This paper presents mechanism, which helps agents detecting adaptation requirements dynamically at run time, and an Trace&Trigger, which is an adaptation framework for agent organizations. It consists of an event-tracing-based monitoring mechanism that provides organizational agents with information related to the costs and benefits of carrying out an adaptation process at each moment of the execution. This framework intends to overcome some of the problems that are present in other approaches by allowing the dynamic specification of the information that has to be retrieved by each agent at each moment for adaptation deliberation, avoiding the transference of useless information for adaptation deliberation. This framework has been integrated in the Magentix2 multiagent platform. In order to test its performance benefits for any agent organization, an example based on a market scenario is also presentedThis work has been supported by projects TIN2011-27652-C03-01 and TIN2012-36586-C03-01.Alberola Oltra, JM.; Búrdalo Rapa, LA.; Julian Inglada, VJ.; Terrasa Barrena, AM.; García-Fornes, A. (2014). An adaptive framework for monitoring agent organizations. Information Systems Frontiers. 16(2):239-256. https://doi.org/10.1007/s10796-013-9478-xS239256162Abdu, H., Lutfiyya, H., Bauer, M.A. (1999). A model for adaptive monitoring configurations. In Proceedings of the 6th IFIP/IEEE IM conference on network management (pp. 371–384).Aiello, M., Busetta, P., Donà, A., Serafini, L. (2002). Ontological overhearing. In ATAL ’01: Revised papers from the 8th international workshop on intelligent agents VIII (pp. 175–189). London: Springer.Alberola, J.M., Mulet, L., Such, J., García-Fornes, A., Espinosa, A., Botti, V. (2007). Operating system aware multiagent platform design. In Fifth European workshop on multi-agent systems (EUMAS 2007) (pp. 658–667).Alberola, J.M., Julian, V., Garcia-Fornes, A. (2011). Cost-aware reorganization service for multiagent systems. In Proceedings of the 2nd international workshop ITMAS11 (pp. 60–74).Alberola, J.M., Julian, V., Garcia-Fornes, A. (2012). Multi-dimensional transition deliberation for organization adaptation in multiagent systems. In Proceedings of the eleventh international conference on autonomous agents and multiagent systems (AAMAS-12) (pp. 1379–1380).Alberola, J.M., Julian, V., Garcia-Fornes, A. (2013a). Challenges for adaptation in agent societies. Knowledge and Information Systems, (in press).Alberola, J.M., Julian, V., Garcia-Fornes, A. (2013b). Multi-dimensional adaptation in mas organizations. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 43(2), 622–633.Alberola, J.M., Julian, V., Garcia-Fornes, A. (2013c). Using cost-aware transitions for reorganizing multiagent systems. Engineering Applications of Artificial Intelligence, 26(1), 63–75.AOS Group (2008). An agent infrastructure for providing the decision-making capability required for autonomous systems. http://www.agent-software.com .Argente, E., Julian, V., Botti, V. (2009). MAS modeling based on Organizations. In Post-Proceedings 9th international workshop AOSE’08 (vol. 5386, pp. 16–30). Springer.Bellifemine, F., Caire, G., Trucco, T., Rimassa, G., Mungenast, R. (2007). Jade administrator’s guide.Bordini, R., & Hübner, J. (2007). Jason: a Java-based interpreter for an extended version of AgentSpeak.Bordini, R., Hübner, J., Vieira, R. (2005). Jason and the golden fleece of agent-oriented programming. In G. Weiss, R. Bordini, M. Dastani, J. Dix, & A. Fallah Seghrouchni (Eds.), Multiagent systems, artificial societies, and simulated organizations, multi-agent programming (vol. 15, pp. 3–37).Botía, J.A., Hernansáez, J.M., Gomez-Skarmeta, A. (2004). Towards an approach for debugging mas through the analysis of acl messages. In Multiagent system technologies (vol. 3187, p. E1). Lecture Notes in Computer Science.Botía, J.A., Hernansáez, J.M., Gomez-Skarmeta, A. (2007). On the application of clustering techniques to support debugging large-scale multi-agent systems. Programming Multi-Agent Systems, 4411, 217–227.Bou, E., López-Sánchez, M., Rodríguez-Aguilar, J.A. (2006). Adaptation of autonomic electronic institutions through norms and institutional agents. In Engineering societies in the agents world. Number LNAI 4457 (pp. 300–319). Springer.Bou, E., López-Sánchez, M., Rodríguez-Aguilar, J.A. (2007). Towards self-configuration in autonomic electronic institutions. In COIN 2006 workshops. Number LNAI 4386 (pp. 220–235). Springer.Búrdalo, L., Terrasa, A., Julián, V., García-Fornes. A (2011). Trammas: a tracing model for multiagent systems. Engineering Applications of Artificial Intelligence, 24(7), 1110–1119.Busetta, P., Donà, A., Nori, M. (2002). Channeled multicast for group communications. In AAMAS ’02: Proceedings of the first international joint conference on autonomous agents and multiagent systems (pp. 1280–1287). ACM: New York.Campos, J., López-Sánchez, M., Esteva, M. (2009). Assistance layer, a step forward in Multi-Agent Systems Coordination Support. In International joint conference on autonomous agents and multiagent systems (AAMAS) (pp. 1301–1302).Campos, J., Esteva, M., Lopez-Sanchez, M., Morales, J., Salamo, M. (2011). Organisational adaptation of multi-agent systems in a peer-to-peer scenario. Computing, 91, 169–215.Carvalho, G., Almeida, H., Gatti, M., Vinicius, G., Paes, R., Perkusich, A., Lucena, C. (2006). Dynamic law evolution in governance mechanisms for open multi-agent systems. In 2nd Workshop on software engineering for agent-oriented systems.Collis, J., Ndumu, D., Nwana, H., Lee, L. (1998). The zeus agent building tool-kit. BT Technology Journal, 16(3), 60–68.DeLoach, S., Oyenan, W., Matson, E. (2008). A capabilities-based model for adaptive organizations. Autonomous Agents and Multi-Agent Systems, 16, 13–56.Deokar, A.V., & El-Gayar, O.F. (2011). Decision-enabled dynamic process management for networked enterprises. Information Systems Frontiers, 13(5), 655–668.Dignum, V.(Ed.). (2009). Multi-agent systems: semantics and dynamics of organizational models. IGI Global.Dignum, F., & Vreeswijk, G. (2004). Advances in Agent Communication. In Towards a testbed for multi-party dialogues, lecture notes in computer science (vol. 2922, pp. 212–230). Berlin/Heidelberg: Springer.Dignum, V., Dignum, F., Sonenberg, L. (2004). Towards dynamic reorganization of agent societies. In Proceedings of workshop on coordination in emergent agent societies (pp. 22–27).Esparcia, S., & Argente, E. (2011). Formalizing virtual organizations. In 3rd International conference on agents and artificial intelligence (ICAART 2011) (vol. 2, pp. 84–93). INSTICC.Fogués, R.L., Alberola, J.M., Such, J.M., Espinosa, A., Garcia-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).Gaston, M.E., & desJardins, M. (2005). Agent-organized networks for dynamic team formation. In Proceedings of the 4th international joint conference on autonomous agents and multiagent systems (AAMAS05) (pp. 230–237).Guessoum, Z., Ziane, M., Faci, N. (2004). Monitoring and organizational-level adaptation of multi-agent systems. In Proceedings of AAMAS 2004, (pp. 514–521). ACM.Hoogendoorn, M., & Treur, J. (2006). An adaptive multi-agent organization model based on dynamic role allocation. In Proceedings of the IAT ’06 (pp. 474–481).Hübner, J.F., Sichman, J.S., Boissier, O. (2004). Using the MOISE+ for a cooperative framework of MAS reorganisation. In Proceedings of the 17th Brazilian symposium on artificial intelligence (SBIA04) (vol. 3171, pp. 506–515).Kaminka, G.A., Tambe, D.V.P.M., Pynadath, D.V., Tambe, M. (2002). Monitoring teams by overhearing: a multi-agent plan-recognition approach. Journal of Artificial Intelligence Research, 17.Kota, R., Gibbins, N., Jennings, N.R. (2009). Decentralised structural adaptation in agent organisations. In Organized adaptation in multi-agent systems (pp. 54–71).Kota, R., Gibbins, N., Jennings, N.R. (2012). Decentralised approaches for self-adaptation in agent organisations. In ACM transactions on autonomous and adaptive systems (pp. 1–28).Legras, F., & Tessier, C. (2003). Lotto: group formation by overhearing in large teams. In AAMAS ’03: Proceedings of the second international joint conference on autonomous agents and multiagent systems (pp. 425–432). New York: ACM.Legras Onera, F. (2002). Using overhearing for local group formation. In AAMAS ’02 workshop 7 teamwork and coalition formation (pp. 8–15).Li, C., & Li, L. (2012). Collaboration among mobile agents for efficient energy allocation in mobile grid. Information Systems Frontiers, 14(3), 711–723.Luck, M., McBurney, P., Shehory, O., Willmott, S. (2005). Agent technology: computing as interaction (A Roadmap for Agent Based Computing). University of Southampton.Malouf, R. (1995). Towards an analysis of multi-party discourse. http://citeseerx.ist.psu.edu/%5Cviewdoc/summary . doi: 10.1.1.8.4024 .Mathieu, P., Routier, J.C., Secq, Y. (2002a). Dynamic organization of multi-agent systems. In Proceedings of the 1st international joint conference on autonomous agents and multiagent systems (AAMAS02) (pp. 451–452).Mathieu, P., Routier, J.-C., Secq, Y. (2002b). Principles for dynamic multi-agent organizations. In Proceedings of the 5th Pacific Rim international workshop on multi agents: intelligent agents and multi-agent systems (PRIMA02) (pp. 109–122).Omicini, A., Ricci, A., Viroli, M. (2008). Artifacts in the A&A meta-model for multi-agent systems. Autonomous Agents and Multi-Agent Systems, 17(3), 432–456.Padgham, L., Winikoff, M., Poutakidis, D. (2005). Adding debugging support to the prometheus methodology. Engineering Applications of Artificial Intelligence, 18(2), 173–190.Platon, E., Sabouret, N., Honiden, S. (2006). Overhearing and direct interactions: point of view of an active environment. In Environments for multi-agent systems II (p. 121–138). Springer.Pokahr, A., & Braubach, L. (2007). Jadex tool guide - release 0.96.Rebollo, M., Giret, A., Argente, E., Carrascosa, C., Corchado, J.M., Fernandez, A., Julian, V. (2009). On the road to an abstract architecture for open virtual organizations. In Proceedings of the 10th international work-conference on Art. Neural Networks (pp. 642–650).Ringold, P., Alegria, J., Czaplewski, R., Mulder, B., Tolle, T., Burnett, K. (1996). Adaptive monitoring design for ecosystem management. Ecological Applications, 6(3), 745–747.Rossi, S., Rossi, S., Busetta, P. (2004). Towards monitoring of group interactions and social roles via overhearing. Proceedings of CIA-04, 3191, 47–61.Routier, J., Mathieu, P., Secq, Y. (2001). Dynamic skill learning: a support to agent evolution, pp. 25–32.Sanchez-Anguix, V., Espinosa, A., Hernandez, L., Garcia-Fornes, A. (2009). Mamsy: a management tool for multi-agent systems. In 7th International conference on practical applications of agents and multi-agent systems (PAAMS 2009) (pp. 130–139).Saunier, J., Balbo, F., Badeig, F. (2007). Environment as active support of interaction. In Environments for Multi-Agent Systems III (pp. 87–105). Springer.Serrano, E., Gómez-Sanz, J.J., Botía, J.A., Pavón, J. (2009). Intelligent data analysis applied to debug complex software systems. Neurocomputing, 72, 2785–2795.Tichý, P., & Slechta, P. (2006). Java Sniffer 2.7 User Manual.Vázquez-Salceda, J., Dignum, V., Dignum, F. (2005). Organizing multiagent systems. Autonomous Agents and Multi-Agent Systems, 11, 307–360.Wang, Z.G., & Liang, X.H. (2006). A graph based simulation of reorganization in multi-agent systems.Weyns, D., Haesevoets, R., Helleboogh, A., Holvoet, T., Joosen, W. (2010). The MACODO middleware for context-driven dynamic agent organizations. ACM Transaction on Autonomous and Adaptive Systems, 5, 3:1–3:28.Weyns, D., Omicini, A., Odell, J. (2007). Environment as a first class abstraction in multiagent systems. In Autonomous agents and multi-agent systems (pp. 5–30)

Magentix 2 User's Manual

USER’S MANUAL. Version 2.1.0. January 2015Magentix2 is an agent platform for open Multiagent Systems. Its main objective is to bring agent technology to real domains: business, industry, logistics, e-commerce, health-care, etc. Magentix2 platform is proposed as a continuation of the first Magentix platform. The final goal is to extend the functionalities of Magentix, providing new services and tools to allow the secure and optimized management of open Multiagent Systems. Nowadays, Magentix2 provides support at three levels: - Organization level, technologies and techniques related to agent societies. - Interaction level, technologies and techniques related to communications between agents. - Agent level, technologies and techniques related to individual agents (such as reasoningand learning). Thus, Magentix2 platform uses technologies with the necessary capacity to cope with the dynamism of the system topology and with flexible interactions, which are both natural consequences of the distributed and autonomous nature of its components. In this sense, the platform has been extended in order to support flexible interaction protocols and conversations, indirect communication and interactions among agent organizations. Moreover, other important aspects cover by the Magentix2 project are the security issues.Botti Navarro, VJ.; Argente Villaplana, E.; Alemany Bordera, J.; Bellver Faus, J.; Búrdalo Rapa, LA.; Carrascosa Casamayor, C.; Criado Pacheco, N.... (2015). Magentix 2 User's Manual. http://hdl.handle.net/10251/4845