The Current State of Normative Agent-Based Systems

Recent years have seen an increase in the application of ideas from the social sciences to computational systems. Nowhere has this been more pronounced than in the domain of multiagent systems. Because multiagent systems are composed of multiple individual agents interacting with each other many parallels can be drawn to human and animal societies. One of the main challenges currently faced in multiagent systems research is that of social control. In particular, how can open multiagent systems be configured and organized given their constantly changing structure? One leading solution is to employ the use of social norms. In human societies, social norms are essential to regulation, coordination, and cooperation. The current trend of thinking is that these same principles can be applied to agent societies, of which multiagent systems are one type. In this article, we provide an introduction to and present a holistic viewpoint of the state of normative computing (computational solutions that employ ideas based on social norms.) To accomplish this, we (1) introduce social norms and their application to agent-based systems; (2) identify and describe a normative process abstracted from the existing research; and (3) discuss future directions for research in normative multiagent computing. The intent of this paper is to introduce new researchers to the ideas that underlie normative computing and survey the existing state of the art, as well as provide direction for future research.Norms, Normative Agents, Agents, Agent-Based System, Agent-Based Simulation, Agent-Based Modeling

Towards next generation coordination infrastructures

Coordination infrastructures play a central role in the engineering of multiagent systems. Since the advent of agent technology, research on coordination infrastructures has produced a significant number of infrastructures with varying features. In this paper, we review the the state-of-the-art coordination infrastructures with the purpose of identifying open research challenges that next generation coordination infrastructures should address. Our analysis concludes that next generation coordination infrastructures must address a number of challenges: (i) to become socially aware, by facilitating human interaction within a MAS; (ii) to assist agents in their decision making by providing decision support that helps them reduce the scope of reasoning and facilitates the achievement of their goals; and (iii) to increase openness to support on-line, fully decentralised design and execution. Furthermore, we identify some promising approaches in the literature, together with the research issues worth investigating, to cope with such challenges. © Cambridge University Press, 2015.The work presented in this paper has been partially funded by projects EVE (TIN2009-14702-C02-01), AT (CSD2007-0022), and the Generalitat of Catalunya grant 2009-SGR-1434Peer Reviewe

Adaptation strategies for self-organising electronic institutions

For large-scale systems and networks embedded in highly dynamic, volatile, and unpredictable environments, self-adaptive and self-organising (SASO) algorithms have been proposed as solutions to the problems introduced by this dynamism, volatility, and unpredictability. In open systems it cannot be guaranteed that an adaptive mechanism that works well in isolation will work well — or at all — in combination with others. In complexity science the emergence of systemic, or macro-level, properties from individual, or micro-level, interactions is addressed through mathematical modelling and simulation. Intermediate meso-level structuration has been proposed as a method for controlling the macro-level system outcomes, through the study of how the application of certain policies, or norms, can affect adaptation and organisation at various levels of the system. In this context, this thesis describes the specification and implementation of an adaptive affective anticipatory agent model for the individual micro level, and a self-organising distributed institutional consensus algorithm for the group meso level. Situated in an intelligent transportation system, the agent model represents an adaptive decision-making system for safe driving, and the consensus algorithm allows the vehicles to self-organise agreement on values necessary for the maintenance of “platoons” of vehicles travelling down a motorway. Experiments were performed using each mechanism in isolation to demonstrate its effectiveness. A computational testbed has been built on a multi-agent simulator to examine the interaction between the two given adaptation mechanisms. Experiments involving various differing combinations of the mechanisms are performed, and the effect of these combinations on the macro-level system properties is measured. Both beneficial and pernicious interactions are observed; the experimental results are analysed in an attempt to understand these interactions. The analysis is performed through a formalism which enables the causes for the various interactions to be understood. The formalism takes into account the methods by which the SASO mechanisms are composed, at what level of the system they operate, on which parts of the system they operate, and how they interact with the population of the system. It is suggested that this formalism could serve as the starting point for an analytic method and experimental tools for a future systems theory of adaptation.Open Acces

Towards next generation coordination infrastructures

Coordination infrastructures play a central role in the engineering of multiagent systems. Since the advent of agent technology, research on coordination infrastructures has produced a significant number of infrastructures with varying features. In this paper, we review the the state-of-the-art coordination infrastructures with the purpose of identifying open research challenges that next generation coordination infrastructures should address. Our analysis concludes that next generation coordination infrastructures must address a number of challenges: (i) to become socially aware, by facilitating human interaction within a MAS; (ii) to assist agents in their decision making by providing decision support that helps them reduce the scope of reasoning and facilitates the achievement of their goals; and (iii) to increase openness to support on-line, fully decentralised design and execution. Furthermore, we identify some promising approaches in the literature, together with the research issues worth investigating, to cope with such challenges

Challenges for adaptation in agent societies

The final publication is available at Springer via http://dx.doi.org/[insert DOIAdaptation in multiagent systems societies provides a paradigm for allowing these societies to change dynamically in order to satisfy the current requirements of the system. This support is especially required for the next generation of systems that focus on open, dynamic, and adaptive applications. In this paper, we analyze the current state of the art regarding approaches that tackle the adaptation issue in these agent societies. We survey the most relevant works up to now in order to highlight the most remarkable features according to what they support and how this support is provided. In order to compare these approaches, we also identify different characteristics of the adaptation process that are grouped in different phases. Finally, we discuss some of the most important considerations about the analyzed approaches, and we provide some interesting guidelines as open issues that should be required in future developments.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, the European Cooperation in the field of Scientific and Technical Research IC0801 AT, and projects TIN2009-13839-C03-01 and TIN2011-27652-C03-01.Alberola Oltra, JM.; Julian Inglada, VJ.; García-Fornes, A. (2014). Challenges for adaptation in agent societies. Knowledge and Information Systems. 38(1):1-34. https://doi.org/10.1007/s10115-012-0565-yS134381Aamodt A, Plaza E (1994) Case-based reasoning; foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59Abdallah S, Lesser V (2007) Multiagent reinforcement learning and self-organization in a network of agents. In: Proceedings of the sixth international joint conference on autonomous agents and multi-agent systems, pp 172–179Abdu H, Lutfiyya H, Bauer MA (1999) A model for adaptive monitoring configurations. In: Proceedings of the VI IFIP/IEEE IM conference on network management, pp 371–384Alberola JM, Julian V, Garcia-Fornes A (2011) A cost-based transition approach for multiagent systems reorganization. In: Proceedings of the 10th international conference on aut. agents and MAS (AAMAS11), pp 1221–1222Alberola JM, Julian V, Garcia-Fornes A (2012) Multi-dimensional transition deliberation for organization adaptation in multiagent systems. In: Proceedings of the 11th international conference on aut. agents and MAS (AAMAS12) (in press)Argente E, Julian V, Botti V (2006) Multi-agent system development based on organizations. Electron Notes Theor Comput Sci 160(3):55–71Argente E, Botti V, Carrascosa C, Giret A, Julian V, Rebollo M (2011) An abstract architecture for virtual organizations: the Thomas approach. Knowl Inf Syst 29(2):379–403Ashford SJ, Taylor MS (1990) Adaptation to work transitions. An integrative approach. Res Pers Hum Resour Manag 8:1–39Ashford SJ, Blatt R, Walle DV (2003) Reflections on the looking glass: a review of research on feedback-seeking behavior in organizations. J Manag 29(6):773–799Astley WG, Van de Ven AH (1983) Central perspectives and debates in organization theory. Adm Sci Q 28(2):245–273Bond AH, Gasser L (1988) A survey of distributed artificial intelligence readings in distributed artificial intelligence. Morgan Kaufmann, Los AltosBou E, López-Sánchez M, Rodríguez-Aguilar JA (2006) Adaptation of autonomic electronic institutions through norms and institutional agents In: Engineering societies in the agents world. Number LNAI 445, Springer, Dublin, pp 300–319Bou E, López-Sánchez M, Rodríguez-Aguilar JA (2007) Towards self-configuration in autonomic electronic institutions. In: COIN 2006 workshops. Number LNAI 4386, pp 220–235Bou E, López-Sánchez M, Rodríguez-Aguilar JA (2008) Using case-based reasoning in autonomic electronic institutions. In: Proceedings of the 2007 international conference on coordination, organizations, institutions, and norms in agent systems III, pp 125–138Brett JM, Feldman DC, Weingart LR (1990) Feedback-seeking behavior of new hires and job changers. J Manag 16:737–749Bulka B, Gaston ME, desJardins M (2007) Local strategy learning in networked multi-agent team formation. Auton Agents Multi-Agent Syst 15(1):29–45Campos J, López-Sánchez M, Esteva M (2009) Assistance layer, a step forward in multi-agent systems. In: Coordination support international joint conference on autonomous agents and multiagent systems (AAMAS), pp 1301–1302Campos 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):169–215Carley KM, and Gasser L (1999) Computational organization theory. Multiagent systems: a modern approach to distributed artificial intelligence. MIT Press, Cambridge, pp 299–330Carvalho 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: Second workshop on software engineering for agent-oriented systemsCernuzzi L, Zambonelli F (2011) Adaptive organizational changes in agent-oriented methodologies. Knowl Eng Rev 26(2):175–190Cheng BH, Lemos R, Giese H, Inverardi P, Magee J (2009) Software engineering for self-adaptive systems: a research roadmap, pp 1–26Corkill DD, Lesser VR (1983) The use of meta-level control for coordination in a distributed problem solving networks. In: Proceedings of the eighth international joint conference on artificial intelligence. IEEE Computer Society Press, pp 748–756Corkill DD, Lander SE (1998) Diversity in agent organizations. Object Mag 8(4):41–47de Paz JF, Bajo J, González A, Rodríguez S, Corchado JM (2012) Combining case-based reasoning systems and support vector regression to evaluate the atmosphere-ocean interaction. Knowl Inf Syst 30(1):155–177DeLoach SA, Matson E (2004) An organizational model for designing adaptive multiagent systems. In: The AAAI-04 workshop on agent organizations: theory and practice (AOTP), pp 66–73DeLoach SA, Oyeman W, Matson E (2008) A capabilities-based model for adaptive organizations. Auton Agents Multi-Agent Syst 16:13–56Dignum V, Dignum F (2001) Modelling agent societies: co-ordination frameworks and institutions progress in artificial intelligence. LNAI 2258, pp 191–204Dignum V (2004) A model for organizational interaction: based on agents, founded in logic. PhD dissertation, Universiteit Utrecht. SIKS dissertation series 2004-1Dignum V, Dignum F, Sonenberg L (2004) Towards dynamic reorganization of agent societies. In: Proceedings of the workshop on coordination in emergent agent societies, pp 22–27Dignum V, Dignum F (2006) Exploring congruence between organizational structure and task performance: a simulation approach coordination, organization, institutions and norms in agent systems I. In: Proceedings of the ANIREM ’05/OOOP ’05, pp 213–230Dignum V, Dignum F (2007) A logic for agent organizations. In: Proceedings of the multi-agent logics, languages, and organisations federated workshops (MALLOW ’007), formal approaches to multi-agent systems (FAMAS ’007) workshopFox MS (1981) Formalizing virtual organizations. IEEE Transact Syst Man Cybern 11(1):70–80Gaston ME, desJardins M (2005) Agent-organized networks for dynamic team formation. In: Proceedings of the fourth international joint conference on autonomous agents and multiagent systems, pp 230–237Gaston ME, desJardins M (2008) The effect of network structure on dynamic team formation in multi-agent systems. Comput Intell 24(2):122–157Norbert G, Philippe M (1997) The reorganization of societies of autonomous agents. In: MAAMAW-97. Springer, London, pp 98–111Goldman CV, Rosenschein JS (1997) Evolving organizations of agents American association for artificial intelligence. In: Multiagent learning workshop at AAAI97Greve HR (1998) Performance, aspirations, and risky organizational change. Adm Sci Quart 43(1):58–86Guessoum Z, Ziane M, Faci N (2004) Monitoring and organizational-level adaptation of multi-agent systems. In: Proceedings of the AAMAS ’04, pp 514–521Hoogendoorn M, Treur J (2006) An adaptive multi-agent organization model based on dynamic role allocation. In: Proceedings of the IAT ’06, pp 474–481Horling B, Benyo B, Lesser V (1999) Using self-diagnosis to adapt organizational structures. In: Proceedings of the 5th international conference on autonomous agents, pp 529–536Horling B, Lesser V (2005) A survey of multi-agent organizational paradigms. Knowl Eng Rev 19(4): 281–316Hrebiniak LG, Joyce WF (1985) Organizational adaptation: strategic choice and environmental determinism. Adm Sci Quart 30(3):336–349Hübner JF, Sichman JS, Boissier O (2002) MOISE+: towards a structural, functional, and deontic model for MAS organization. In: Proceedings of the first international joint conference on autonomous agents and multiagent systems, pp 501–502Hübner JF, Sichman JS, Boissier O (2004) Using the MOISE+ for a cooperative framework of MAS reorganisation. In: Proceedings of the 17th Brazilian symposium on artificial intelligence (SBIA ’04), vol 3171, pp 506–515Hübner JF, Boissier O, Sichman JS (2005) Specifying E-alliance contract dynamics through the MOISE + reorganisation process Anais do V Encontro Nacional de Inteligde Inteligncia Artificial (ENIA 2005)Jennings NR (2001) An agent-based approach for building complex software systems. Commun ACM 44(4):35–41Kamboj S, Decker KS (2006) Organizational self-design in semi-dynamic environments In: 2007 IJCAI workshop on agent organizations: models and simulations (AOMS@IJCAI), pp 335–337Katz D, Kahn RL (1966) The social psychology of organizations. Wiley, New YorkKelly D, Amburgey TL (1991) Organizational inertia and momentum: a dynamic model of strategic change. Acad Manag J 34(3):591–612Kephart J, Chess DM (2003) The vision of autonomic computing. Computer 36(1):41–50Kim DH (1993) The link between individual and organizational learning. Sloan Manag Rev 35(1):37–50Kota R, Gibbins N, Jennings NR (2009a) Decentralised structural adaptation in agent organisations organized adaptation in multi-agent systems, pp 54–71Kota R, Gibbins N, Jennings NR (2009b) Self-organising agent organisations. In: Proceedings of the 8th international conference on autonomous agents and multiagent systems (AAMAS 2009)Kota R, Gibbins N, Jennings NR (2012) Decentralised approaches for self-adaptation in agent organisations. ACM Trans Auton Adapt Syst 7(1):1–28Kotter J, Schlesinger L (1979) Choosing strategies for change. Harv Bus Rev 106–1145Lesser VR (1998) Reflections on the nature of multi-agent coordination and its implications for an agent architecture. Auton Agents Multi-Agent Syst 89–111Levitt B, March JG (1988) Organizational learning. Annu Rev Sociol 14:319–340Luck M, McBurney P, Shehory O, Willmott S (2005) Agent technology: computing as interaction (a roadmap for agent based computing)Mathieu P, Routier JC, Secq Y (2002a) Dynamic organization of multi-agent systems. In: Proceedings of the first international joint conference on autonomous agents and multiagent systems: part 1, pp 451–452Mathieu P, Routier JC, 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, pp 109–122Matson E, DeLoach S (2003) Using dynamic capability evaluation to organize a team of cooperative, autonomous robots. In: Proceedings of the 2003 international conference on artificial intelligence (IC-AI ’03), Las Vegas, pp 23–26Matson E, DeLoach S (2004) Enabling intra-robotic capabilities adaptation using an organization-based multiagent system. ICRA, pp 2135–2140Matson E, DeLoach S (2005) Formal transition in agent organizations. In: IEEE international conference on knowledge intensive multiagent systems (KIMAS ’05)Matson E, Bhatnagar R (2006) Properties of capability based agent organization transition. In: Proceedings of the IEEE/WIC/ACM international conference on intelligent agent technology IAT ’06, pp 59–65Morales J, López-Sánchez M, Esteva, M (2011) Using experience to generate new regulations. In: Proceedings of the twenty-second international joint conference on artificial Intelligence (IJCAI-11), pp 307–312Muhlestein D, Lim S (2011) Online learning with social computing based interest sharing. Knowl Inf Syst 26(1):31–58Nair R, Tambe M, Marsella S (2003) Role allocation and reallocation in multiagent teams: towards a practical analysis. In: Proceedings of the second AAMAS ’03, pp 552–559Orlikowski WJ (1996) Improvising organizational transformation over time: a situated change perspective. Inf Syst Res 7(1):63–92Panait L, Luke S (2005) Cooperative multi-agent learning: the state of the art. Auton Agents Multi-Agent Syst 11:387–434Ringold PL, Alegria J, Czaplewski RL, Mulder BS, Tolle T, Burnett K (1996) Adaptive monitoring design for ecosystem management. Ecol Appl 6(3):745–747Routier J, Mathieu P, Secq Y (2001) Dynamic skill learning: a support to agent evolution. In: Proceedings of the artificial intelligence and the simulation of behaviour symposium on adaptive agents and multi-agent systems (AISB ’01), pp 25–32Scott RW (2002) Organizations: rational, natural, and open systems, 5th edn. Prentice Hall International, New YorkSeelam A (2009) Reorganization of massive multiagent systems: MOTL/O http://books.google.es/books?id=R-s8cgAACAAJ . Southern Illinois University CarbondaleSo Y, Durfee EH (1993) An organizational self-design model for organizational change. In: AAAI93 workshop on AI and theories of groups and oranizations, pp 8–15So Y, Durfee EH (1998) Designing organizations for computational agents. Simulating organizations. MIT Press, Cambridge, pp 47–64Schwaninger M (2000) A theory for optimal organization. Technical report. Institute of Management at the University of St. Gallen, SwitzerlandTantipathananandh C, Berger-Wolf TY (2011) Finding communities in dynamic social networks. In: IEEE 11th international conference on data mining 2011, pp 1236–1241Wang Z, Liang X (2006) A graph based simulation of reorganization in multi-agent systems. In: IEEE WICACM international conference on intelligent agent technology, pp 129–132Wang D, Tse Q, Zhou Y (2011) A decentralized search engine for dynamic web communities. Knowl Inf Syst 26(1):105–125Weick KE (1979) The social psychology of organizing, 2nd edn. Addison-Wesley, ReadingWeyns D, Haesevoets R, Helleboogh A, Holvoet T, Joosen W (2010a) The MACODO middleware for context-driven dynamic agent organizations. ACM Transact Auton Adapt Syst 3:1–3:28Weyns D, Malek S, Andersson J (2010b) FORMS: a formal reference model for self-adaptation. In: Proceedings of the 7th international conference on autonomic computing, pp 205–214Weyns D, Georgeff M (2010) Self-adaptation using multiagent systems. IEEE Softw 27(1):86–91Zhong C (2006) An investigation of reorganization algorithms. Master-thesi

Embodied Organizations: a Unifying Perspective in Programming Agents, Organizations and Environments

http://ceur-ws.org/Vol-627/coin_7.pdfInternational audienceMAS research pushes the notion of openness related to systems combining heterogeneous computational entities. Typically, those entities answer to different purposes and functions and their integration is a crucial issue. Starting from a comprehensive approach in developing agents, organizations and environments, this paper devises an integrated approach and describes a unifying programming model. It introduces the notion of embodied organization, which is described first focusing on the main entities as separate concerns; and, second, establishing different interaction styles aimed to seamlessly integrate the various entities in a coherent system. An integration framework, built on top of Jason, CArtAgO and Moise (as programming platforms for agents, environments and organizations resp.) is described as a suitable technology to build embodied organizations in practice

Reorganization in Dynamic Agent Societies

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