covid
Buscar en
Revista Iberoamericana de Automática e Informática Industrial RIAI
Toda la web
Inicio Revista Iberoamericana de Automática e Informática Industrial RIAI Modelado Basado en Agentes: un Enfoque desde la Ingeniería de Sistemas
Información de la revista
Vol. 12. Núm. 3.
Páginas 304-312 (julio - septiembre 2015)
Compartir
Compartir
Descargar PDF
Más opciones de artículo
Visitas
7183
Vol. 12. Núm. 3.
Páginas 304-312 (julio - septiembre 2015)
Open Access
Modelado Basado en Agentes: un Enfoque desde la Ingeniería de Sistemas
Agent-Based Modelling: an Approach from the Systems Engineering.
Visitas
7183
María Pereda, Jesús M. Zamarreño
Autor para correspondencia
jesusm@autom.uva.es

Autor para correspondencia.
Departamento de Ingeniería de Sistemas y Automática, EII, Universidad de Valladolid, C/ Doctor Mergelina s/n, 47011, Valladolid, España
Este artículo ha recibido

Under a Creative Commons license
Información del artículo
Resumen
Texto completo
Bibliografía
Descargar PDF
Estadísticas
Resumen

El modelado basado en agentes (ABM, Agent Based Modeling) es una técnica de modelado que está siendo explotada con gran éxito en áreas como la ecología, ciencias sociales, economía, etc. Sin embargo, su uso como técnica de modelado en el campo de la Automática es más bien testimonial. En este artículo mostramos cómo se puede abordar el modelado basado en agentes desde el punto de vista de la Ingeniería de Sistemas y Automática y las particularidades que tiene como herramienta de modelado. Asimismo, proponemos una descripción matemática de los modelos basados en agentes que ilustramos con un par de ejemplos.

Palabras clave:
Agentes
Modelado dinámico
Ingeniería de sistemas
Espacio de estados
Representaciones conceptuales.
Abstract

Agent-Based Modelling (ABM) is a modelling technique with great success in fields like ecology, social sciences, economy, etc. However, it is not so widespread in the Automatic field. In this paper, we present how to deal with ABM from the point of view of the System Engineering and Automatic Control field and the specific issues to take into account as modelling technique. Besides, we propose a mathematical description that is illustrated through two simple examples.

Keywords:
Agents
Dynamic modelling
Systems engineering
State space
Conceptual representations.
Referencias
[Borshchev and Filippov, 2003]
Borshchev, A., Filippov, A., July 2004. From system dynamics and discrete event to practical agent based modeling: Reasons, techniques, tools. In: Proceedings of the 22nd International Conference of the System Dynamics Society. Oxford, England. Collier, N., 2003. RePast: An Extensible Framework for Agent Simulation. http://repast.sourceforge.net/(last visited August 2013).
[Dong et al., 2008]
Dong, J., xin Yin, Y., xiang Peng, K., 2008. Industrial process coordinated and controlled based on multi-agent technology. Systems Engineering - Theory & Practice 28 (10), 119-124. DOI: http://dx.doi.org/10.1016/S1874-8651(10)60004-X.
[Galán et al., 2009]
Galán, J.M., Izquierdo, L.R., Izquierdo, S.S., Santos, J.I., del Olmo, R., López-Paredes, A., Edmonds, B., 2009. Errors and artefacts in agent-based modelling. Journal of Artificial Societies and Social Simulation 12 (1), 1. URL: http://jasss.soc.surrey.ac.uk/12/1/1.html.
[Gilbert, 2008]
Gilbert, G.N., 2008. Agent-based models. Quantitative applications in the social sciences. Sage.
[Grimm et al., 2006]
Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S.K., Huse, G., Huth, A., Jepsen, J.U., Jorgensen, C., Mooij, W.M., Muller, B., Pe’er, G., Piou, C., Railsback, S.F., Robbins, A.M., Robbins, M.M., Rossmanith, E., Ruger, N., Strand, E., Souissi, S., Stillman, R.A., Vabo, R., Visser, U., Deangelis, D.L., 2006. A standard protocol for describing individual-based and agent-based models. Ecological Modelling 198, 115-126.
[Grimm et al., 2010]
Grimm, V., Berger, U., DeAngelis, D.L., Polhill, J.G., Giske, J., Railsback, S.F., Nov. 2010. The ODD protocol: A review and first update. Ecological Modelling 221 (23), 2760-2768. DOI: http://dx.doi.org/10.1016/j.ecolmodel.2010.08.019.
[Hinkelmann et al., 2010]
Hinkelmann, F., Murrugarra, D., Jarrah, A.S., Laubenbacher, R.C., 2010. A mathematical framework for agent based models of complex biological networks. Computing Research repository abs/1006.0408. URL: http://dblp.uni-trier.de/db/journals/corr/corr1006. html#abs-1006-0408.
[Hu et al., 2013]
Hu, H.-X., Liu, A., Xuan, Q., Yu, L., Xie, G., 2013. Second-order consensus of multi-agent systems in the cooperation-competition network with switching topologies: A time-delayed impulsive control approach. Systems & Control Letters 62 (12), 1125-1135. DOI: http://dx.doi.org/10.1016/j.sysconle.2013.09.002.
[Innocenti et al., 2007]
Innocenti, B., López, B., Salvi, J., 2007. A multi-agent architecture with cooperative fuzzy control for a mobile robot. Robotics and Autonomous Systems 55 (12), 881-891, robotics and Autonomous Systems in the 50th Anniversary of Artificial Intelligence Campus Multidisciplinary in Perception and Intelligence. DOI: http://dx.doi.org/10.1016/j.robot.2007.07.007.
[Izquierdo et al., 2008]
Izquierdo, L., Galán, J.M., Santos, J.I., del Olmo, R., 2008. Modelado de sistemas complejos mediante simulación basada en agentes y mediante dinámica de sistemas. Empiria: Revista de metodología de ciencias sociales 16, 85-112.
[Leombruni and Richiardi, 2005]
Leombruni, R., Richiardi, M., Sep. 2005. Why are economists sceptical about agent-based simulations? Physica A: Statistical Mechanics and its Applications 355 (1), 103-109. DOI: http://dx.doi.org/10.1016/j.physa.2005.02.072.
[Lo, 2012]
Lo, S.K., 2012. A collaborative multi-agent message transmission mechanism in intelligent transportation system - a smart freeway example. Information Sciences 184 (1), 246-265. DOI: http://dx.doi.org/10.1016/j.ins.2011.08.024.
[Luck et al., 2005]
Luck, M., McBurney, P., Shehory, O., Willmott, S., 2005. Agent Technology: Computing as Interaction (A Roadmap for Agent Based Computing). AgentLink.
[Luke et al., 2003]
Luke, S., Balan, G.C., Panait, L., Cioffi-Revilla, C., Paus, S., 2003. MASON: A Java Multi-Agent Simulation Library. In: Macal, C.M., North, M., Sallach, D. (Eds.), Proceedings of Agent 2003, Conference on Challenges in Social Simulation. Argonne National Laboratory.
[Macal and North, 2006]
Macal, C.M., North, M.J., Dec. 2006. Tutorial on agent-based modeling and simulation part 2: How to model with agents. In: Winter Simulation Conference, 2006. WSC 06. Proceedings of the. pp. 73-83. DOI: http://dx.doi.org/10.1109/wsc.2006.323040.
[MATLAB, 2010]
MATLAB, 2010. version 7.10.0 (R2010b). The MathWorks Inc., Natick, Massachusetts.
[Minar et al., 1996]
Minar, N., Burkhart, R.and Langton, C., Askenazi, M., 1996. The swarm simulation system: A toolkit for building multi-agent simulations. Santa Fe Institute working paper 96-06-042. Swarm available at http://www.swarm. org (last visited August 2013).
[Pereda and Zamarreño, 2011]
Pereda, M., Zamarreño, J.M., Jun. 2011. Agent-based modeling of an activated sludge process in a batch reactor. In: 2011 19th Mediterranean Conference on Control & Automation (MED). IEEE, Corfu, pp. 1128-1133. DOI: http://dx.doi.org/10.1109/MED. 2011.5983027.
[Pereda and Zamarreño, 2014]
Pereda, M., Zamarreño, J.M., 2014. “Thermostat II” (Version 3). CoMSES Computational Model Library. Retrieved from: https://www.openabm. org/model/4234/version/3 (last visited June 2014).
[Potter et al., 1996]
Potter, B., Sinclair, J., Till, D., 1996. Introduction to Formal Specification and Z (2nd Edition). Prentice Hall PTR.
[Rahmandad and Sterman, 2008]
Rahmandad, H., Sterman, J., 2008. Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science 54 (5), 998-1014. DOI: http://dx.doi.org/10.1287/mnsc.1070.0787.
[Reynolds, 1987]
Reynolds, C.W., Aug. 1987. Flocks, herds and schools: A distributed behavioral model. SIGGRAPH Computer Graphics 21 (4), 25-34. DOI: http://dx.doi.org/10.1145/37402.37406.
[Schelling, 1969]
Schelling, T.C., 1969. Models of segregation. The American Economic Review 59 (2), 488-493.
[Schieritz and Milling, 2003]
Schieritz, N., Milling, P.M., 2003. Modeling the forest or modeling the trees. comparison of sd and ab simulation. In: Proceedings of the 21st International Conference of the System Dynamics Society.
[Torsun, 1995]
Torsun, I., 1995. Foundations of Intelligent Knowledge-Based Systems. Library and Information Science. Academic Press Limited.
[Van Dyke Parunak et al., 1998]
Van Dyke Parunak, H., Savit, R., Riolo, R.L., 1998. Agent-based modeling vs. equation-based modeling: A case study and users¿ guide. In: Sichman, J.S. a., Conte, R., Gilbert, N. (Eds.), Multi-Agent Systems and Agent-Based Simulation. Vol. 1534 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 10-25. DOI: http://dx.doi.org/10.1007/10692956 2.
[Wen et al., 2013]
Wen, G., Hu, G., Yu, W., Cao, J., Chen, G., 2013. Consensus tracking for higher-order multi-agent systems with switching directed topologies and occasionally missing control inputs. Systems & Control Letters 62 (12), 1151-1158. DOI: http://dx.doi.org/10.1016/j.sysconle.2013.09.009.
[Wilensky, 1997]
Wilensky, U., 1997. NetLogo Segregation model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/models/Segregation (last visited March 2013).
[Wilensky, 1998]
Wilensky, U., 1998. NetLogo Thermostat model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/models/Thermostat (last visited March 2013).
[Wilensky, 1999]
Wilensky, U., 1999. NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl. northwestern.edu/netlogo/(last visited March 2013).
[Wilensky, 2002]
Wilensky, U., 2002. NetLogo Crystallization Basic model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/models/CrystallizationBasic (last visited March 2013).
[Wilensky, 2007]
Wilensky, U., 2007. NetLogo Solid Diffusion model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/models/SolidDiffusion (last visited March 2013).
[Yu and Wang, 2013]
Yu, L., Wang, J., 2013. Robust cooperative control for multi-agent systems via distributed output regulation. Systems & Control Letters 62 (11), 1049-1056. DOI: http://dx.doi.org/10.1016/j.sysconle.2013.08.005.
[Zhu, 2014]
Zhu, J., 2014. Stabilization and synchronization for a heterogeneous multiagent system via harmonic control. Systems & Control Letters 66 (0), 1-7. DOI: http://dx.doi.org/10.1016/j.sysconle.2013.12.019.
Descargar PDF
Opciones de artículo