The organisation of sociality: a manifesto for a new science of multi-agent systems

In this paper, we pose and motivate a challenge, namely the need for a new science of multi-agent systems. We propose that this new science should be grounded, theoretically on a richer conception of sociality, and methodologically on the extensive use of computational modelling for real-world applications and social simulations. Here, the steps we set forth towards meeting that challenge are mainly theoretical. In this respect, we provide a new model of multi-agent systems that reflects a fully explicated conception of cognition, both at the individual and the collective level. Finally, the mechanisms and principles underpinning the model will be examined with particular emphasis on the contributions provided by contemporary organisation theory

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

Player agency in interactive narrative: audience, actor & author

The question motivating this review paper is, how can computer-based interactive narrative be used as a constructivist learn- ing activity? The paper proposes that player agency can be used to link interactive narrative to learner agency in constructivist theory, and to classify approaches to interactive narrative. The traditional question driving research in interactive narrative is, ‘how can an in- teractive narrative deal with a high degree of player agency, while maintaining a coherent and well-formed narrative?’ This question derives from an Aristotelian approach to interactive narrative that, as the question shows, is inherently antagonistic to player agency. Within this approach, player agency must be restricted and manip- ulated to maintain the narrative. Two alternative approaches based on Brecht’s Epic Theatre and Boal’s Theatre of the Oppressed are reviewed. If a Boalian approach to interactive narrative is taken the conflict between narrative and player agency dissolves. The question that emerges from this approach is quite different from the traditional question above, and presents a more useful approach to applying in- teractive narrative as a constructivist learning activity

A survey on engineering approaches for self-adaptive systems (extended version)

The complexity of information systems is increasing in recent years, leading to increased effort for maintenance and configuration. Self-adaptive systems (SASs) address this issue. Due to new computing trends, such as pervasive computing, miniaturization of IT leads to mobile devices with the emerging need for context adaptation. Therefore, it is beneficial that devices are able to adapt context. Hence, we propose to extend the definition of SASs and include context adaptation. This paper presents a taxonomy of self-adaptation and a survey on engineering SASs. Based on the taxonomy and the survey, we motivate a new perspective on SAS including context adaptation

Sistema de teste auto-adaptativo baseado em modelo para SOA dinâmico

Orientadores: Eliane Martins, Andrea CeccarelliDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Arquitetura orientada a serviços (SOA) é um padrão de design popular para implemen- tação de serviços web devido à interoperabilidade, escalabilidade e reuso de soluções de software que promove. Os serviços que usam essa arquitetura precisam operar em um am- biente altamente dinâmico, entretanto quanto mais a complexidade desses serviços cresce menos os métodos tradicionais de validação se mostram viáveis. Aplicações baseadas em arquitetura orientada a serviços podem evoluir e mudar du- rante a execução. Por conta disso testes offline não asseguram completamente o compor- tamento correto de um sistema em tempo de execução. Por essa razão, a necessidade de tecnicas diferentes para validar o comportamento adequado de uma aplicação SOA durante o seu ciclo de vida são necessárias, por isso testes online executados durante o funcionamento serão usados nesse projeto. O objetivo do projeto é de aplicar técnicas de testes baseados em modelos para gerar e executar casos de testes relevantes em aplicações SOA durante seu tempo de execu- ção. Para alcançar esse objetivo uma estrura de teste online autoadaptativa baseada em modelos foi idealizada. Testes baseados em modelos podem ser gerados de maneira offline ou online. Nos testes offline, os casos de teste são gerados antes do sistema entrar em execução. Já nos testes online, os casos de teste são gerados e aplicados concomitantemente, e as saídas produzidas pela aplicação em teste definem o próximo passo a ser realizado. Quando uma evolução é detectada em um serviço monitorado uma atualização no modelo da aplicação alvo é executada, seguido pela geração e execução de casos de testes online. Mais precisamente, quatro componentes foram integrados em um circuito autoadap- tativo: um serviço de monitoramento, um serviço de criação de modelos, um serviço de geração de casos de teste baseado em modelos e um serviço de teste. As caracteristicas da estrutura de teste foram testadas em três cenários que foram executados em uma aplicação SOA orquestrada por BPEL, chamada jSeduite. Este trabalho é um esforço para entender as restrições e limitações de teste de soft- ware para aplicações SOA, e apresenta análises e soluções para alguns dos problemas encontrados durante a pesquisaAbstract: Service Oriented Architecture (SOA) is a popular design pattern to build web services be- cause of the interoperability, scalability, and reuse of software solutions that it promotes. The services using this architecture need to operate in a highly dynamic environment, but as the complexity of these services grows, traditional validation processes become less feasible. SOA applications can evolve and change during their execution, and offline tests do not completely assure the correct behavior of the system during its execution. There- fore there is a need of techniques to validate the proper behaviour of SOA applications during the SOA lifecycle. Because of that, in this project online testing will be used. The project goal is to employ model-based testing techniques to generate and execute relevant test cases to SOA applications during runtime. In order to achieve this goal a self-adaptive model-based online testing framework was designed. Tests based on models can be generated offline and online. Offline test are generated before the system execution. Online tests are generated and performed concomitantly, and the output produced by the application under test defines the next step to be performed. when our solution detects that a monitored service evolves, the model of the target service is updated, and online test case generation and execution is performed. More specifically, four components were integrated in a self-adaptive loop: a mon- itoring service, a model generator service, a model based testing service and a testing platform. The testing framework had its features tested in three scenarios that were performed in a SOA application orchestrated by BPEL, called jSeduite. This work is an effort to understand the constraints and limitations of the software testing on SOA applications, and present analysis and solutions to some of the problems found during the researchMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

Ergonomics of the Operative Field in Paediatric Minimal Access Surgery

Imperial Users onl

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

Investigating diurnal changes in the chromatin organization of A. thaliana and the involvement of linker histone H1.

Light is not only a crucial source of energy for plants, but also delivers important information about the environment. It is perceived by a variety of photoreceptors that act to fine tune the plant cell metabolism and gene expression program. This enables adaptation to the environment. It remains unclear how light impacts gene expression, but it is thought to involve large-scale epigenetic and chromatin changes. During the diurnal rhythm genes are expressed in a coordinated manner and influence various physiological processes. This is thought to be controlled, at least partially, by changes in the 3D chromatin organization and by epigenetic modifications. A key regulator of genome packaging is the linker histone H1, which impacts molecular and spatial chromatin organization. Depletion of H1 in A. thaliana has been shown to cause aberrant root development in plants grown under different light regimes. However, it remains unclear how chromatin architecture is changing along the diurnal rhythm and what role H1 plays in this process. Our cytological analysis of chromatin features in A. thaliana revealed rhythmic abundance of H3K27me3, H3K4me3 and RNA Polymerase II S2P levels along the diurnal rhythm. Mutant analyses revealed that H1 contributes regulating the diurnal abundance of histone modifications and RNA Pol II activity, but only moderately influences Pol II S2P distributions. Major changes occur before light is turned on or off, suggesting a circadian entrainment to re-organize these chromatin features. Collectively, our results suggest a possible implication of H1 in regulating subtle, but significant chromatin dynamics at large scale, hence uncovering a novel function for linker histones in plants