Abstract
In most multiagent-based simulation (MABS) frameworks, a scheduler activates the agents who compute their context and decide the action to execute. This context computation by the agents is executed based on information about themselves, the other agents and the objects of the environment that are accessible to them. The issue here is the identification of the information subsets that are relevant for each agent. This process is time-consuming and is one of the barriers to increased use of MABS for large simulations. Moreover, this process is hidden in the agent behavior and no algorithm has been designed to decrease its cost. We propose a new context model where each subset of information identifying a context is formalized by a so called “filter” and where the filters are clustered in ordered trees. Based on this context model, we also propose an algorithm to find efficiently for each agent their filters following their perceptible information. The agents receive perceptible information, execute our algorithm to know their context and decide which action to execute. Our algorithm is compared to a “classic” one, where the context identification uses no special data structure. Promising results are presented and discussed.
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References
Badeig, F., Balbo, F.: Définition d’un cadre de conception et d’exécution pour la simulation multi-agent. Revue d’Intelligence Artificielle 26(3), 255–280 (2012)
Badeig, F., Balbo, F., Pinson, S.: Contextual activation for agent-based simulation. In: Proceedings of the 21st European Conference on Modelling and Simulation, ECMS (2007)
Béhé, F., Galland, S., Gaud, N., Nicolle, C., Koukam, A.: An ontology-based metamodel for multiagent-based simulations. Simulation Modelling Practice and Theory 40, 64–85 (2014)
Collier, N.: Repast: An extensible framework for agent simulation, vol. 36. The University of Chicago’s Social Science Research (2003)
Abowd, G.D., Dey, A.K.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304–307. Springer, Heidelberg (1999)
Farenc, N., Boulic, R., Thalmann, D.: An informed environment dedicated to the simulation of virtual humans in urban context. In: Proceedings of EUROGRAPHICS 1999, pp. 309–318 (1999)
Ferber, J., Gutknecht, O.: Madkit: A generic multi-agent platform. In: 4th International Conference on Autonomous Agents, pp. 78–79 (2000)
Forgy, C.L.: Rete: A fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligence 19, 17–37 (1982)
Kubera, Y., Mathieu, P., Picault, S.: Interaction-oriented agent simulations: From theory to implementation. In: Ghallab, M., Spyropoulos, C., Fakotakis, N., Avouris, N. (eds.) Proceedings of the 18th European Conference on Artificial Intelligence (ECAI 2008), pp. 383–387. IOS Press (2008)
Michel, F.: Translating agent perception computations into environmental processes in multi-agent-based simulations: A means for integrating graphics processing unit programming within usual agent-based simulation platforms. Systems Research and Behavioral Science 30(6), 703–715 (2013)
Sierhuis, M., Clancey, W.J., Van Hoof, R.J.: Brahms: a multi-agent modelling environment for simulating work processes and practices. International Journal of Simulation and Process Modelling 3(3), 134–152 (2007)
Šišlák, D., Rehák, M., Pěchouček, M., Rollo, M., Pavlíček, D.: A-globe: Agent development platform with inaccessibility and mobility support. In: Software Agent-Based Applications, Platforms and Development Kits, pp. 21–46. Springer (2005)
Wagner, G.: AOR modelling and simulation: Towards a general architecture for agent-based discrete event simulation. In: Giorgini, P., Henderson-Sellers, B., Winikoff, M. (eds.) AOIS 2003. LNCS (LNAI), vol. 3030, pp. 174–188. Springer, Heidelberg (2004)
Warden, T., Porzel, R., Gehrke, J.D., Herzog, O., Langer, H., Malaka, R.: Towards ontology-based multiagent simulations: The plasma approach. In: 24th European Conference on Modelling and Simulation (ECMS 2010). European Council for Modelling and Simulation, pp. 50–56 (2010)
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Balbo, F., Zargayouna, M., Badeig, F. (2014). A Tree-Based Context Model to Optimize Multiagent Simulation. In: Müller, J.P., Weyrich, M., Bazzan, A.L.C. (eds) Multiagent System Technologies. MATES 2014. Lecture Notes in Computer Science(), vol 8732. Springer, Cham. https://doi.org/10.1007/978-3-319-11584-9_17
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DOI: https://doi.org/10.1007/978-3-319-11584-9_17
Publisher Name: Springer, Cham
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