Advances in infrastructures and tools for multiagent systems

In the last few years, information system technologies have focused on solving challenges in order to develop distributed applications. Distributed systems can be viewed as collections of service-provider and ser vice-consumer components interlinked by dynamically defined workflows (Luck and McBurney 2008).Alberola Oltra, JM.; Botti Navarro, VJ.; Such Aparicio, JM. (2014). Advances in infrastructures and tools for multiagent systems. Information Systems Frontiers. 16:163-167. doi:10.1007/s10796-014-9493-6S16316716Alberola, J. M., Búrdalo, L., Julián, V., Terrasa, A., & García-Fornes, A. (2014). An adaptive framework for monitoring agent organizations. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9478-x .Alfonso, B., Botti, V., Garrido, A., & Giret, A. (2014). A MAS-based infrastructure for negotiation and its application to a water-right market. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9443-8 .Andrighetto, G., Castelfranchi, C., Mayor, E., McBreen, J., López-Sánchez, M., & Parsons, S. (2013). (Social) norm dynamics. In G. Andrighetto, G. Governatori, P. Noriega, & L. W. van der Torre (Eds.), Normative multi-agent systems (pp. 135–170). Dagstuhl: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik.Baarslag, T., Fujita, K., Gerding, E. H., Hindriks, K., Ito, T., Jennings, N. R., et al. (2013). Evaluating practical negotiating agents: results and analysis of the 2011 international competition. Artificial Intelligence, 198, 73–103.Boissier, O., Bordini, R. H., Hübner, J. F., Ricci, A., & Santi, A. (2013). Multi-agent oriented programming with JaCaMo. Science of Computer Programming, 78(6), 747–761.Campos, J., Esteva, M., López-Sánchez, M., Morales, J., & Salamó, M. (2011). Organisational adaptation of multi-agent systems in a peer-to-peer scenario. Computing, 91(2), 169–215.Carrera, A., Iglesias, C. A., & Garijo, M. (2014). Beast methodology: an agile testing methodology for multi-agent systems based on behaviour driven development. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9438-5 .Criado, N., Such, J. M., & Botti, V. (2014). Norm reasoning services. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9444-7 .Del Val, E., Rebollo, M., & Botti, V. (2014). Enhancing decentralized service discovery in open service-oriented multi-agent systems. Journal of Autonomous Agents and Multi-Agent Systems, 28(1), 1–30.Denti, E., Omicini, A., & Ricci, A. (2002). Coordination tools for MAS development and deployment. Applied Artificial Intelligence, 16(9–10), 721–752.Dignum, V., & Dignum, F. (2012). A logic of agent organizations. Logic Journal of IGPL, 20(1), 283–316.Ferber, J., & Gutknecht, O. (1998). A meta-model for the analysis and design of organizations in multi-agent systems. In Multi agent systems. Proceedings. International Conference on (pp. 128–135). IEEE.Fogués, R. L., Such, J. M., Espinosa, A., & Garcia-Fornes, A. (2014). BFF: a tool for eliciting tie strength and user communities in social networking services. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9453-6 .Garcia, E., Giret, A., & Botti, V. (2011). Evaluating software engineering techniques for developing complex systems with multiagent approaches. Information and Software Technology, 53(5), 494–506.Garcia-Fornes, A., Hübner, J., Omicini, A., Rodriguez-Aguilar, J., & Botti, V. (2011). Infrastructures and tools for multiagent systems for the new generation of distributed systems. Engineering Applications of Articial Intelligence, 24(7), 1095–1097.Jennings, N., Faratin, P., Lomuscio, A., Parsons, S., Sierra, C., & Wooldridge, M. (2001). Automated negotiation: prospects, methods and challenges. International Journal of Group Decision and Negotiation, 10(2), 199–215.Jung, Y., Kim, M., Masoumzadeh, A., & Joshi, J. B. (2012). A survey of security issue in multi-agent systems. Artificial Intelligence Review, 37(3), 239–260.Kota, R., Gibbins, N., & Jennings, N. R. (2012). Decentralized approaches for self-adaptation in agent organizations. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 7(1), 1.Kraus, S. (1997). Negotiation and cooperation in multi-agent environments. Artificial Intelligence, 94(1), 79–97.Lin, Y. I., Chou, Y. W., Shiau, J. Y., & Chu, C. H. (2013). Multi-agent negotiation based on price schedules algorithm for distributed collaborative design. Journal of Intelligent Manufacturing, 24(3), 545–557.Luck, M., & McBurney, P. (2008). Computing as interaction: agent and agreement technologies.Luck, M., McBurney, P., Shehory, O., & Willmott, S. (2005). Agent technology: Computing as interaction (A roadmap for agent based computing). AgentLink.Ossowski, S., & Menezes, R. (2006). On coordination and its significance to distributed and multiagent systems. Concurrency and Computation: Practice and Experience, 18(4), 359–370.Ossowski, S., Sierra, C., & Botti. (2013). Agreement technologies: A computing perspective. In Agreement Technologies (pp. 3–16). Springer Netherlands.Pinyol, I., & Sabater-Mir, J. (2013). Computational trust and reputation models for open multi-agent systems: a review. Artificial Intelligence Review, 40(1), 1–25.Ricci, A., Piunti, M., & Viroli, M. (2011). Environment programming in multi-agent systems: an artifact-based perspective. Autonomous Agents and Multi-Agent Systems, 23(2), 158–192.Sierra, C., & Debenham, J. (2006). Trust and honour in information-based agency. In Proceedings of the 5th international conference on autonomous agents and multi agent systems, (p. 1225–1232). New York: ACM.Sierra, C., Botti, V., & Ossowski, S. (2011). Agreement computing. KI-Knstliche Intelligenz, 25(1), 57–61.Vasconcelos, W., García-Camino, A., Gaertner, D., Rodríguez-Aguilar, J. A., & Noriega, P. (2012). Distributed norm management for multi-agent systems. Expert Systems with Applications, 39(5), 5990–5999.Wooldridge, M. (2002). An introduction to multiagent systems. New York: Wiley.Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: theory and practice. Knowledge Engineering Review, 10(2), 115–152

Agreement Computing

[EN] In this paper we introduce the concept of Agreement Computing, motivate the central role that the concept of agreement plays in open software systems and discuss a number of research challenges that need to be addressed to make the agreement computing vision a reality.Research supported by the Agreement Technologies CONSOLIDER project under contract CSD2007-0022 and INGENIO 2010 and by the Agreement Technologies COST Action, IC0801.Sierra Garcia, C.; Botti Navarro, VJ.; Ossowski, DS. (2011). Agreement Computing. KI - Künstliche Intelligenz. 25(1):57-61. https://doi.org/10.1007/s13218-010-0070-yS5761251Arcos JL, Esteva M, Noriega P, Rodríguez JA, Sierra C (2005) Engineering open environments with electronic institutions. Eng Appl Artif Intell 18(2):191–204Boella G, Noriega P, Pigozzi G, Verhagen H (2009) In: Dagstuhl seminar proceedings 09121: normative multi-agent systems.Henrik G, Wright V (1963) Norm and action, a logical enquiry. Routledge and Kegan Paul, LondonHermenegildo M, Albert E, López-García P, Puebla G (2005) Abstraction carrying code and resource-awareness. In: Principle and practice of declarative programming. ACM Press, New YorkJennings N, Faratin P, Lomuscio A, Parsons S, Sierra C, Wooldridge M (2001) Automated negotiation: prospects methods and challenges. Group Decis Negot 10(2):199–215Jøsang A, Ismail R, Boyd C (2007) A survey of trust and reputation systems for online service provision. Decis Support Syst 43(2):618–644Kalfoglou Y, Schorlemmer M (2003) IF-Map: an ontology-mapping method based on information-flow theory. In: Spaccapietra S, March S, Aberer K (eds) Journal on data semantics I. Lecture notes in computer science, vol 2800. Springer, Heidelberg, pp 98–127Ko RKL, Lee SSG, Lee EW (2009) Business process management (bpm) standards: a survey. Bus Process Manag J 15(5):744–791Kraus S (1997) Negotiation and cooperation in multi-agent environments. Artif Intell 94(1–2):79–97March J (1996) A preface to understanding how decisions happen in organizations. In: Organizational decision-making, Cambridge University Press, CambridgeNecula GC, Lee P (1996) Proof-carrying code. Tech repRoss A (1968) Directives and norms. Humanities, Atlantic HighlandsSierra C, Debenham J (2006) Trust and honour in information-based agency. In: Proceedings of the 5th international conference on autonomous agents and multiagent systems. ACM Press, New York, pp 1225–1232Simon HA Administrative behavior. Free Press (1997)Vasirani M, Ossowski S (2009) A market-inspired approach to reservation-based urban road traffic management. In: Proceedings of the 8th international conference on autonomous agents and multiagent systems, IFAAMAS, pp. 617–62

Asymptotically idempotent aggregation operators for trust management in multi-agent systems

The study of trust management in multi-agent system, especially distributed, has grown over the last years. Trust is a complex subject that has no general consensus in literature, but has emerged the importance of reasoning about it computationally. Reputation systems takes into consideration the history of an entity’s actions/behavior in order to compute trust, collecting and aggregating ratings from members in a community. In this scenario the aggregation problem becomes fundamental, in particular depending on the environment. In this paper we describe a technique based on a class of asymptotically idempotent aggregation operators, suitable particulary for distributed anonymous environments

Federated Embedded Systems – a review of the literature in related fields

This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways

Blockchain Solutions for Multi-Agent Robotic Systems: Related Work and Open Questions

The possibilities of decentralization and immutability make blockchain probably one of the most breakthrough and promising technological innovations in recent years. This paper presents an overview, analysis, and classification of possible blockchain solutions for practical tasks facing multi-agent robotic systems. The paper discusses blockchain-based applications that demonstrate how distributed ledger can be used to extend the existing number of research platforms and libraries for multi-agent robotic systems.Comment: 5 pages, FRUCT-2019 conference pape

Rational Trust Modeling

Trust models are widely used in various computer science disciplines. The main purpose of a trust model is to continuously measure trustworthiness of a set of entities based on their behaviors. In this article, the novel notion of "rational trust modeling" is introduced by bridging trust management and game theory. Note that trust models/reputation systems have been used in game theory (e.g., repeated games) for a long time, however, game theory has not been utilized in the process of trust model construction; this is where the novelty of our approach comes from. In our proposed setting, the designer of a trust model assumes that the players who intend to utilize the model are rational/selfish, i.e., they decide to become trustworthy or untrustworthy based on the utility that they can gain. In other words, the players are incentivized (or penalized) by the model itself to act properly. The problem of trust management can be then approached by game theoretical analyses and solution concepts such as Nash equilibrium. Although rationality might be built-in in some existing trust models, we intend to formalize the notion of rational trust modeling from the designer's perspective. This approach will result in two fascinating outcomes. First of all, the designer of a trust model can incentivise trustworthiness in the first place by incorporating proper parameters into the trust function, which can be later utilized among selfish players in strategic trust-based interactions (e.g., e-commerce scenarios). Furthermore, using a rational trust model, we can prevent many well-known attacks on trust models. These two prominent properties also help us to predict behavior of the players in subsequent steps by game theoretical analyses