103,961 research outputs found

    From Physical to Virtual: Widening the Perspective on Multi-Agent Environments

    Full text link
    The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-23850-0_9Since more than a decade, the environment is seen as a key element when analyzing, developing or deploying Multi-Agent Systems (MAS) applications. Especially, for the development of multi-agent platforms it has become a key concept, similarly to many application in the area of location-based, distributed systems. An emerging, prominent application area for MAS is related to Virtual Environments. The underlying technology has evolved in a way, that these applications have grown out of science fiction novels till research papers and even real applications. Even more, current technologies enable MAS to be key components of such virtual environments. In this paper, we widen the concept of the environment of a MAS to encompass new and mixed physical, virtual, simulated, etc. forms of environments. We analyze currently most interesting application domains based on three dimensions: the way different "realities" are mixed via the environment, the underlying natures of agents, the possible forms and sophistication of interactions. In addition to this characterization, we discuss how this widened concept of possible environments influences the support it can give for developing applications in the respective domains.Carrascosa Casamayor, C.; Klugl, F.; Ricci, A.; Boissier, O. (2015). From Physical to Virtual: Widening the Perspective on Multi-Agent Environments. En Agent Environments for Multi-Agent Systems IV. 4th International Workshop, E4MAS 2014 - 10 Years Later, Paris, France, May 6, 2014. 133-146. https://doi.org/10.1007/978-3-319-23850-0_9S133146Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: a review. ACM Comput. Surv. 43(3), 16:1–16:43 (2011)Argente, E., Boissier, O., Carrascosa, C., Fornara, N., McBurney, P., Noriega, P., Ricci, A., Sabater-Mir, J., et al.: The role of the environment in agreement technologies. AI Rev. 39(1), 21–38 (2013)Barreteau, O., et al.: Our companion modelling approach. J. Artif. Soc. Soc. Simul. 6(1), 1–6 (2003)Boissier, O., Bordini, R.H., HĂŒbner, J.F., Ricci, A., Santi, A.: Multi-agent oriented programming with jacamo. Sci. Comput. Program. 78(6), 747–761 (2013)Burdea, G., Coiffet, P.: Virtual Reality Technology. Wiley, New York (2003)Castelfranchi, C., Pezzullo, G., Tummolini, L.: Behavioral implicit communication (BIC): communicating with smart environments via our practical behavior and its traces. Int. J. Ambient Comput. Intell. 2(1), 1–12 (2010)Castelfranchi, C., Piunti, M., Ricci, A., Tummolini, L.: AMI systems as agent-based mirror worlds: bridging humans and agents through stigmergy. In: Bosse, T. (ed.) Agents and Ambient Intelligence, Ambient Intelligence and Smart Environments, pp. 17–31. IOS Press, Amsterdam (2012)Ferber, J.: Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison Wesley Longman, Harlow (1999)Gelernter, D.: Mirror Worlds - or the Day Software Puts the Universe in a Shoebox: How it Will Happen and What it Will Mean. Oxford University Press, New York (1992)Gibson, W.: Neuromancer. Ace, New York (1984)KlĂŒgl, F., Fehler, M., Herrler, R.: About the role of the environment in multi-agent simulations. In: Weyns, D., Van Parunak, H.D., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 127–149. Springer, Heidelberg (2005)Krueger, M.: Artificial Reality II. Addison-Wesley, New York (1991)Luck, M., Aylett, R.: Applying artificial intelligence to virtual reality: intelligent virtual environments. Appl. Artif. Intell. 14(1), 3–32 (2000)Dorigo, M., Floreano, D., Gambardella, L.M., et al.: Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robot. Autom. Mag. 20(4), 60–71 (2013)Milgram, P., Kishino, A.F.: Taxonomy of mixed reality visual displays. IEICE Trans. Inf. Syst. E77–D(12), 1321–1329 (1994)Olsson, T., Salo, M.: Online user survey on current mobile augmented reality applications. In: Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2011, pp. 75–84. IEEE Computer Society, Washington, DC, USA (2011)Saunier, J., Balbo, F., Pinson, S.: A formal model of communication and context awareness in multiagent systems. J. Logic Lang. Inform. 23(2), 219–247 (2014)Stephenson, N.: Snow Crash. Bantam Books, New York (1992)Tummolini, L., Castelfranchi, C.: Trace signals: the meanings of stigmergy. In: Weyns, D., Van Parunak, H.D., Michel, F. (eds.) E4MAS 2006. LNCS (LNAI), vol. 4389, pp. 141–156. Springer, Heidelberg (2007)Weyns, D., Omicini, A., Odell, J.: Environment as a first class abstraction in multiagent systems. Auton. Agent. Multi-Agent Syst. 14(1), 5–30 (2007)Weyns, D., Schelfthout, K., Holvoet, T., Lefever, T.: Decentralized control of e’gv transportation systems. In: Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 67–74. ACM (2005)Weyns, D., Schumacher, M., Ricci, A., Viroli, M., Holvoet, T.: Environments in multiagent systems. Knowl. Eng. Rev. 20(2), 127–141 (2005

    Multi-agent simulations for emergency situations in an airport scenario

    Get PDF
    This paper presents a multi-agent framework using Net- Logo to simulate humanand collective behaviors during emergency evacuations. Emergency situationappears when an unexpected event occurs. In indoor emergency situation, evacuation plans defined by facility manager explain procedure and safety ways tofollow in an emergency situation. A critical and public scenario is an airportwhere there is an everyday transit of thousands of people. In this scenario theimportance is related with incidents statistics regarding overcrowding andcrushing in public buildings. Simulation has the objective of evaluating buildinglayouts considering several possible configurations. Agents could be based onreactive behavior like avoid danger or follow other agent, or in deliberative behaviorbased on BDI model. This tool provides decision support in a real emergencyscenario like an airport, analyzing alternative solutions to the evacuationprocess.Publicad

    Towards a goal-oriented agent-based simulation framework for high-performance computing

    Get PDF
    Currently, agent-based simulation frameworks force the user to choose between simulations involving a large number of agents (at the expense of limited agent reasoning capability) or simulations including agents with increased reasoning capabilities (at the expense of a limited number of agents per simulation). This paper describes a first attempt at putting goal-oriented agents into large agentbased (micro-)simulations. We discuss a model for goal-oriented agents in HighPerformance Computing (HPC) and then briefly discuss its implementation in PyCOMPSs (a library that eases the parallelisation of tasks) to build such a platform that benefits from a large number of agents with the capacity to execute complex cognitive agents.Peer ReviewedPostprint (author's final draft

    Talking Nets: A Multi-Agent Connectionist Approach to Communication and Trust between Individuals

    Get PDF
    A multi-agent connectionist model is proposed that consists of a collection of individual recurrent networks that communicate with each other, and as such is a network of networks. The individual recurrent networks simulate the process of information uptake, integration and memorization within individual agents, while the communication of beliefs and opinions between agents is propagated along connections between the individual networks. A crucial aspect in belief updating based on information from other agents is the trust in the information provided. In the model, trust is determined by the consistency with the receiving agents’ existing beliefs, and results in changes of the connections between individual networks, called trust weights. Thus activation spreading and weight change between individual networks is analogous to standard connectionist processes, although trust weights take a specific function. Specifically, they lead to a selective propagation and thus filtering out of less reliable information, and they implement Grice’s (1975) maxims of quality and quantity in communication. The unique contribution of communicative mechanisms beyond intra-personal processing of individual networks was explored in simulations of key phenomena involving persuasive communication and polarization, lexical acquisition, spreading of stereotypes and rumors, and a lack of sharing unique information in group decisions

    Agents for educational games and simulations

    Get PDF
    This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

    Exploring synergies between farmers' livelihoods, forest conservation and social equity participatory simulations for creative negotiation in Thailand highlands

    Get PDF
    En dĂ©pit de l'usage croissant du concept de dĂ©veloppement durable, les interactions entre ses trois piliers (environnementaux, Ă©conomiques et sociaux) sont plus souvent pensĂ©es en termes de compromis qu'en termes de synergies. À partir d'une Ă©tude de cas sur un conflit autour de l'accĂšs aux ressources fonciĂšres et forestiĂšres entre un parc national en cours d'Ă©tablissement et deux villages dans les hautes terres du Nord de la ThaĂŻlande, cet article montre que le concept de nĂ©gociation intĂ©grative peut ĂȘtre intĂ©ressant pour rĂ©vĂ©ler des synergies potentielles entre la prĂ©servation de l'environnement, la subsistance des agriculteurs, et l'Ă©quitĂ© sociale. Dans cette Ă©tude de cas, des sessions participatives de simulations multi-agents ont favorisĂ© l'Ă©mergence d'un mode de nĂ©gociation crĂ©atif et intĂ©gratif entre diffĂ©rents types d'agriculteurs, des forestiers et des agents du parc national. Ces simulations ont permis aux diffĂ©rents protagonistes de reformuler le problĂšme en jeu et de rĂ©aliser qu'ils avaient des intĂ©rĂȘts en commun, notamment dans la limitation de la dĂ©forestation et la gestion des produits forestiers de collecte. (RĂ©sumĂ© d'auteur
    • 

    corecore