Reinforcement Learning for Nash Equilibrium Generation

Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.We propose a new conceptual multi-agent framework which, given a game with an undesirable Nash equilibrium, will almost surely generate a new Nash equilibrium at some predetennined, more desirable pure action profile. The agent(s) targeted for reinforcement learn independently according to a standard model-free algorithm, using internally-generated states corresponding to high-level preference rankings over outcomes. We focus in particular on the case in which the additional reward can be considered as resulting from an internal (re-)appraisal, such that the new equilibrium is stable independent of the continued application of the procedure

FMAP: A platform for the development of distributed multi-agent planning systems

[EN] The development of cooperative Multi-Agent Planning (MAP) solvers in a distributed context encompasses the design and implementation of decentralized algorithms that make use of multi-agent communication protocols. In this paper, we present FMAP, a platform aimed at developing distributed MAP solvers such as MAP-POP, FMAP and MH-FMAP, among others. (C) 2018 Elsevier B.V. All rights reserved.This work is supported by the Spanish MINECO under projects TIN2014-55637-C2-2-R and TIN2017-88476-C2-1-R. The first author was funded by the Spanish SEPE.Torreño Lerma, A.; Sapena Vercher, O.; Onaindia De La Rivaherrera, E. (2018). FMAP: A platform for the development of distributed multi-agent planning systems. Knowledge-Based Systems. 145:166-168. https://doi.org/10.1016/j.knosys.2018.01.013S16616814

Consensus disturbance rejection for Lipschitz nonlinear multi-agent systems with input delay: a DOBC approach

In this paper, a new predictor-based consensus disturbance rejection method is proposed for high-order multi agent systems with Lipschitz nonlinearity and input delay. First, a distributed disturbance observer for consensus control is developed for each agent to estimate the disturbance under the delay constraint. Based on the conventional predictor feedback approach, a non-ideal predictor based control scheme is constructed for each agent by utilizing the estimate of the disturbance and the prediction of the relative state information. Then, rigorous analysis is carried out to ensure that the extra terms associated with disturbances and nonlinear functions are properly considered. Sufficient conditions for the consensus of the multi-agent systems with disturbance rejection are derived based on the analysis in the framework of Lyapunov-Krasovskii functionals. A simulation example is included to demonstrate the performance of the proposed control scheme. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.National Natural Science Foundation of China [61673034]SCI(E)ARTICLE1,SI298-31535

CRiBAC: Community-centric role interaction based access control model

As one of the most efficient solutions to complex and large-scale problems, multi-agent cooperation has been in the limelight for the past few decades. Recently, many research projects have focused on context-aware cooperation to dynamically provide complex services. As cooperation in the multi-agent systems (MASs) becomes more common, guaranteeing the security of such cooperation takes on even greater importance. However, existing security models do not reflect the agents' unique features, including cooperation and context-awareness. In this paper, we propose a Community-based Role interaction-based Access Control model (CRiBAC) to allow secure cooperation in MASs. To do this, we refine and extend our preliminary RiBAC model, which was proposed earlier to support secure interactions among agents, by introducing a new concept of interaction permission, and then extend it to CRiBAC to support community-based cooperation among agents. We analyze potential problems related to interaction permissions and propose two approaches to address them. We also propose an administration model to facilitate administration of CRiBAC policies. Finally, we present the implementation of a prototype system based on a sample scenario to assess the proposed work and show its feasibility. © 2012 Elsevier Ltd. All rights reserved

A language for the execution of graded BDI agents

We are interested in the specification and deployment of multi-agent systems, and particularly we focus on the execution of agents. Along this research line, we have proposed a general model for graded BDI agents, specifying an architecture based on multi-context systems (MCSs) and able to deal with the environment uncertainty (via graded beliefs) and with graded mental proactive attitudes (via desires and intentions). These graded attitudes are represented using appropriate fuzzy modal logics. In this article, we cope with the operational semantics of this agent model. We present a Multi-context calculus, based on Ambient calculus, for the execution of MCSs with its corresponding semantics. This calculus is general enough to support different kinds of MCSs and particularly, we show how a graded BDI agent can be mapped into the language of the calculus. © The Author 2011. Published by Oxford University Press. All rights reserved.The authors are thankful to the anonymous reviewers for their helpful comments for improving the paper. Ana Casali acknowledge partial support by the PID-UNR ING308 project. Llus Godo and Carles Sierra acknowledge partial support by the Spanish project Agreement Technologies (CONSOLIDER CSD2007-0022, INGENIO 2010).Peer Reviewe

Verifying and Synthesising Multi-Agent Systems against One-Goal Strategy Logic Specifications

© Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaa1.org). All rights reserved.Strategy Logic (SL) has recently come to the fore as a useful specification language to reason about multi-agent systems. Its one-goal fragment, or SL[1g], is of particular interest as it strictly subsumes widely used logics such as ATL∗, while maintaining attractive complexity features. In this paper we put forward an automata-based methodology for verifying and synthesising multi-agent systems against specifications given in SL[Ig], We show that the algorithm is sound and optimal from a computational point of view. A key feature of the approach is that all data structures and operations on them can be performed on BDDs. We report on a BDD-based model checker implementing the algorithm and evaluate its performance on the fair process scheduler synthesis

Evaluating how agent methodologies support the specification of the normative environment through the development process

[EN] Due to the increase in collaborative work and the decentralization of processes in many domains, there is an expanding demand for large-scale, flexible and adaptive software systems to support the interactions of people and institutions distributed in heterogeneous environments. Commonly, these software applications should follow specific regulations meaning the actors using them are bound by rights, duties and restrictions. Since this normative environment determines the final design of the software system, it should be considered as an important issue during the design of the system. Some agent-oriented software engineering methodologies deal with the development of normative systems (systems that have a normative environment) by integrating the analysis of the normative environment of a system in the development process. This paper analyses to what extent these methodologies support the analysis and formalisation of the normative environment and highlights some open issues of the topic.This work is partially supported by the PROMETEOII/2013/019, TIN2012-36586-C03-01, FP7-29493, TIN2011-27652-C03-00, CSD2007-00022 projects, and the CASES project within the 7th European Community Framework Program under the grant agreement No 294931.Garcia Marques, ME.; Miles, S.; Luck, M.; Giret Boggino, AS. (2014). Evaluating how agent methodologies support the specification of the normative environment through the development process. Autonomous Agents and Multi-Agent Systems. 1-20. https://doi.org/10.1007/s10458-014-9275-zS120Cossentino, M., Hilaire, V., Molesini, A., & Seidita, V. (Eds.). (2014). Handbook on agent-oriented design processes (Vol. VIII, 569 p. 508 illus.). Berlin: Springer.Akbari, O. (2010). A survey of agent-oriented software engineering paradigm: Towards its industrial acceptance. Journal of Computer Engineering Research, 1, 14–28.Argente, E., Botti, V., Carrascosa, C., Giret, A., Julian, V., & Rebollo, M. (2011). An abstract architecture for virtual organizations: The THOMAS approach. Knowledge and Information Systems, 29(2), 379–403.Argente, E., Botti, V., & Julian, V. (2009). GORMAS: An organizational-oriented methodological guideline for open MAS. In Proceedings of AOSE’09 (pp. 440–449).Argente, E., Botti, V., & Julian, V. (2009). Organizational-oriented methodological guidelines for designing virtual organizations. In Distributed computing, artificial intelligence, bioinformatics, soft computing, and ambient assisted living. Lecture Notes in Computer Science (Vol. 5518, pp. 154–162).Boella, G., Pigozzi, G., & van der Torre, L. (2009). Normative systems in computer science—Ten guidelines for normative multiagent systems. In G. Boella, P. Noriega, G. Pigozzi, & H. Verhagen (Eds.), Normative multi-agent systems, number 09121 in Dagstuhl seminar proceedings.Boella, G., Torre, L., & Verhagen, H. (2006). Introduction to normative multiagent systems. Computational and Mathematical Organization Theory, 12(2–3), 71–79.Bogdanovych, A., Esteva, M., Simoff, S., Sierra, C., & Berger, H. (2008). A methodology for developing multiagent systems as 3d electronic institutions. In M. Luck & L. Padgham (Eds.), Agent-Oriented Software Engineering VIII (Vol. 4951, pp. 103–117). Lecture Notes in Computer Science. Berlin: Springer.Boissier, O., Padget, J., Dignum, V., Lindemann, G., Matson, E., Ossowski, S., Sichman, J., & Vazquez-Salceda, J. (2006). Coordination, organizations, institutions and norms in multi-agent systems. LNCS (LNAI) (Vol. 3913).Bordini, R. H., Fisher, M., Visser, W., & Wooldridge, M. (2006). Verifying multi-agent programs by model checking. In Autonomous agents and multi-agent systems (Vol. 12, pp. 239–256). Hingham, MA: Kluwer Academic Publishers.Botti, V., Garrido, A., Giret, A., & Noriega, P. (2011). The role of MAS as a decision support tool in a water-rights market. In Post-proceedings workshops AAMAS2011 (Vol. 7068, pp. 35–49). Berlin: Springer.Breaux, T. (2009). Exercising due diligence in legal requirements acquisition: A tool-supported, frame-based approach. In Proceedings of the IEEE international requirements engineering conference (pp. 225–230).Breaux, T. D., & Baumer, D. L. (2011). Legally reasonable security requirements: A 10-year ftc retrospective. Computers and Security, 30(4), 178–193.Breaux, T. D., Vail, M. W., & Anton, A. I. (2006). Towards regulatory compliance: Extracting rights and obligations to align requirements with regulations. In Proceedings of the 14th IEEE international requirements engineering conference, RE ’06 (pp. 46–55). Washington, DC: IEEE Computer Society.Bresciani, P., Perini, A., Giorgini, P., Giunchiglia, F., & Mylopoulos, J. (2004). Tropos: An agent-oriented software development methodology. Autonomous Agents and Multi-Agent Systems, 8(3), 203–236.Cardoso, H. L., & Oliveira, E. (2008). A contract model for electronic institutions. In COIN’07: Proceedings of the 2007 international conference on Coordination, organizations, institutions, and norms in agent systems III (pp. 27–40).Castor, A., Pinto, R. C., Silva, C. T. L. L., & Castro, J. (2004). Towards requirement traceability in tropos. In WER (pp. 189–200).Chopra, A., Dalpiaz, F., Giorgini, P., & Mylopoulos, J. (2009). Modeling and reasoning about service-oriented applications via goals and commitments. ICST conference on digital business.Cliffe, O., Vos, M., & Padget, J. (2006). Specifying and analysing agent-based social institutions using answer set programming. In O. Boissier, J. Padget, V. Dignum, G. Lindemann, E. Matson, S. Ossowski, J. Sichman, & J. Vázquez-Salceda (Eds.), Coordination, organizations, institutions, and norms in multi-agent systems. Lecture Notes in Computer Science (Vol. 3913, pp. 99–113). Springer. Berlin.Criado, N., Argente, E., Garrido, A., Gimeno, J. A., Igual, F., Botti, V., Noriega, P., & Giret, A. (2011). Norm enforceability in Electronic Institutions? In Coordination, organizations, institutions, and norms in agent systems VI (Vol. 6541, pp. 250–267). Springer.Dellarocas, C., & Klein, M. (2001). Contractual agent societies. In R. Conte & C. Dellarocas (Eds.), Social order in multiagent systems (Vol. 2, pp. 113–133)., Multiagent Systems, Artificial Societies, and Simulated Organizations New York: Springer.DeLoach, S. A. (2008). Developing a multiagent conference management system using the o-mase process framework. In Proceedings of the international conference on agent-oriented software engineering VIII (pp. 168–181).DeLoach, S. A., & Garcia-Ojeda, J. C. (2010). O-mase; a customisable approach to designing and building complex, adaptive multi-agent systems. International Journal of Agent-Oriented Software Engineering, 4(3), 244–280.DeLoach, S. A., Padgham, L., Perini, A., Susi, A., & Thangarajah, J. (2009). Using three aose toolkits to develop a sample design. International Journal Agent-Oriented Software Engineering, 3, 416–476.Dignum, F., Dignum, V., Thangarajah, J., Padgham, L., & Winikoff, M. (2007). Open agent systems? Eighth international workshop on agent oriented software engineering (AOSE) in AAMAS07.Dignum, V. (2003). A model for organizational interaction:based on agents, founded in logic. PhD thesis, Utrecht University.Dignum, V., Meyer, J., Dignum, F., & Weigand, H. (2003). Formal specification of interaction in agent societies. Formal approaches to agent-based systems (Vol. 2699).Dignum, V., Vazquez-Salceda, J., & Dignum, F. (2005). Omni: Introducing social structure, norms and ontologies into agent organizations. In R. Bordini, M. Dastani, J. Dix, & A. Seghrouchni (Eds.)Programming multi-agent systems. Lecture Notes in Computer Science (Vol. 3346, pp. 181–198). Berlin: Springer.d’Inverno, M., Luck, M., Noriega, P., Rodriguez-Aguilar, J., & Sierra, C. (2012). Communicating open systems, 186, 38–94.Elsenbroich, C., & Gilbert, N. (2014). Agent-based modelling. In Modelling norms (pp. 65–84). Dordrecht: Springer.Esteva, M., Rosell, B., Rodriguez, J. A., & Arcos, J. L. (2004). AMELI: An agent-based middleware for electronic institutions. In AAMAS04 (pp. 236–243).Fenech, S., Pace, G. J., & Schneider, G. (2009). Automatic conflict detection on contracts. In Proceedings of the 6th international colloquium on theoretical aspects of computing, ICTAC ’09 (pp. 200–214).Garbay, C., Badeig, F., & Caelen, J. (2012). Normative multi-agent approach to support collaborative work in distributed tangible environments. In Proceedings of the ACM 2012 conference on computer supported cooperative work companion, CSCW ’12 (pp. 83–86). New York, NY: ACM.Garcia, E., Giret, A., & Botti, V. (2011). Regulated open multi-agent systems based on contracts. In Information Systems Development (pp. 243–255).Garcia, E., Tyson, G., Miles, S., Luck, M., Taweel, A., Staa, T. V., & Delaney, B. (2012). An analysis of agent-oriented engineering of e-health systems. In 13th international eorkshop on sgent-oriented software engineering (AOSE-AAMAS).Garcia, E., Tyson, G., Miles, S., Luck, M., Taweel, A., Staa, T. V., and Delaney, B. (2013). Analysing the Suitability of Multiagent Methodologies for e-Health Systems. In Agent-Oriented Software Engineering XIII, volume 7852, pages 134–150. Springer-Verlag.Garrido, A., Giret, A., Botti, V., & Noriega, P. (2013). mWater, a case study for modeling virtual markets. In New perspectives on agreement technologies (Vol. Law, Gover, pp. 563–579). Springer.Gteau, B., Boissier, O., & Khadraoui, D. (2006). Multi-agent-based support for electronic contracting in virtual enterprises. IFAC Symposium on Information Control Problems in Manufacturing (INCOM), 150(3), 73–91.Hollander, C. D., & Wu, A. S. (2011). The current state of normative agent-based systems. Journal of Artificial Societies and Social Simulation, 14(2), 6.Hsieh, F.-S. (2005). Automated negotiation based on contract net and petri net. In E-commerce and web technologies. Lecture Notes in Computer Science (Vol. 3590, pp. 148–157).Kollingbaum, M., Jureta, I. J., Vasconcelos, W., & Sycara, K. (2008). Automated requirements-driven definition of norms for the regulation of behavior in multi-agent systems. In Proceedings of the AISB 2008 workshop on behaviour regulation in multi-agent systems, Aberdeen, Scotland, U.K., April 2008.Li, T., Balke, T., Vos, M., Satoh, K., & Padget, J. (2013). Detecting conflicts in legal systems. In Y. Motomura, A. Butler, & D. Bekki (Eds.), New Frontiers in Artificial Intelligence (Vol. 7856, pp. 174–189)., Lecture Notes in Computer Science Berlin Heidelberg: Springer.Lomuscio, A., Qu, H., & Solanki, M. (2010) Towards verifying contract regulated service composition. Journal of Autonomous Agents and Multi-Agent Systems (pp. 1–29).Lopez, F., Luck, M., & d’Inverno, M. (2006). A normative framework for agent-based systems. Computational and Mathematical Organization Theory, 12, 227–250.Lpez, F. y, Luck, M., & dInverno, M. (2006). A normative framework for agent-based systems. Computational and Mathematical Organization Theory, 12(2–3), 227–250.Mader, P., & Egyed, A. (2012). Assessing the effect of requirements traceability for software maintenance. In 28th IEEE International Conference on Software Maintenance (ICSM) (pp. 171–180), Sept 2012.Mao, X., & Yu, E. (2005). Organizational and social concepts in agent oriented software engineering. In AOSE IV. Lecture Notes in Artificial Intelligence (Vol. 3382, pp. 184–202).Meyer, J.-J. C., & Wieringa, R. J. (Eds.). (1993). Deontic logic in computer science: Normative system specification. Chichester, UK: Wiley.Okouya, D., & Dignum, V. (2008). Operetta: A prototype tool for the design, analysis and development of multi-agent organizations (demo paper). In AAMAS (pp. 1667–1678).Malone, T. W., Smith J. B., & Olson, G. M. (2001). Coordination theory and collaboration technology. Mahwah, NJ: Lawrence Erlbaum Associates.Oren, N., Panagiotidi, S., Vázquez-Salceda, J., Modgil, S., Luck, M., & Miles, S. (2009). Towards a formalisation of electronic contracting environments. COIN (pp. 156–171).Osman, N., Robertson, D., & Walton, C. (2006). Run-time model checking of interaction and deontic models for multi-agent systems. In AAMAS ’06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (pp. 238–240). New York, NY: ACM.Pace, G., Prisacariu, C., & Schneider, G. (2007). Model checking contracts a case study. In Automated technology for verification and analysis. Lecture Notes in Computer Science (Vol. 4762, pp. 82–97).Rotolo, A., & van der Torre, L. (2011). Rules, agents and norms: Guidelines for rule-based normative multi-agent systems. RuleML Europe, 6826, 52–66.Saeki, M., & Kaiya, H. (2008). Supporting the elicitation of requirements compliant with regulations. In CAiSE ’08 (pp. 228–242).Siena, A., Mylopoulos, J., Perini, A., & Susi, A. (2009). Designing law-compliant software requirements. In Proceedings of the 28th international conference on conceptual modeling, ER ’09 (pp. 472–486).Singh, M. P. Commitments in multiagent systems: Some history, some confusions, some controversies, some prospects.Solaiman, E., Molina-Jimenez, C., & Shrivastav, S. (2003). Model checking correctness properties of electronic contracts. In Service-oriented computing—ICSOC 2003. 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A MAS-based infrastructure for negotiation and its application to a water-right market

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. 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(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). 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Engineering Intelligent Systems for Electrical Engineering and Communications, 13(2), 147–154.Sierra, C., Botti, V., Ossowski, S. (2011). Agreement computing. KI - Künstliche Intelligenz, 25(1), 57–61.Thobani, M. (1997). Formal water markets: why, when and how to introduce tradable water rights. The World Bank Research Observer, 12(2), 161–179

An investigation into the issues of multi-agent data mining

Multi-agent systems (MAS) often deal with complex applications that require distributedproblem solving. In many applications the individual and collective behaviourof the agents depends on the observed data from distributed sources. The field of DistributedData Mining (DDM) deals with these challenges in analyzing distributed dataand offers many algorithmic solutions to perform different data analysis and miningoperations in a fundamentally distributed manner that pays careful attention to the resourceconstraints. Since multi-agent systems are often distributed and agents haveproactive and reactive features, combining DM with MAS for data intensive applicationsis therefore appealing.This Chapter discusses a number of research issues concerned with the use ofMulti-Agent Systems for Data Mining (MADM), also known as agent-driven datamining. The Chapter also examines the issues affecting the design and implementationof a generic and extendible agent-based data mining framework. An ExtendibleMulti-Agent Data mining System (EMADS) Framework for integrating distributeddata sources is presented. This framework achieves high-availability and highperformance without compromising the data integrity and security. © 2010 Nova Science Publishers, Inc. All rights reserved