Abstract
Multi Agent Based Simulation (MABS) has been used mostly in purely social contexts. However, compared to other approaches, e.g., traditional discrete event simulation, object-oriented simulation and dynamic micro simulation, MABS has a number of interesting properties which makes it useful also for other domains. For instance, it supports structure preserving modeling of the simulated reality, simulation of pro-active behavior, parallel computations, and very dynamic simulation scenarios. It is argued that MABS is a useful technique for simulating scenarios also in more technical domains. In particular, this hold for the simulation of technical systems that are distributed and involve complex interaction between humans and machines. To illustrate the advantages of MABS, an application concerning the monitoring and control of intelligent buildings is described.
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References
Basu N. and Pryor R.J.: Growing a Market Economy. Technical Report SAND-97-2093, Sandia National Laboratories, Albuquerque, NM, USA (1997)
Boman M., Davidsson P., Skarmeas N., Clark K., and Gustavsson R.: Energy Saving and Added Customer Value in Intelligent Buildings. In: Third International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology (1998) 505–517
Boman M., Davidsson P., and Younes H.L.: Artificial Decision Making under Uncertainty in Intelligent Buildings, Fifteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, (1999) 65–70
Davidsson P. and Boman M.: Saving Energy and Providing Value Added Services in Intelligent Buildings: A MAS approach. In: Agent Systems, Mobile Agents, and Applications, Springer Verlag (2000) 166–177
Deutschman, D.H., Levin S.A., Devine C., and Buttel L.A.: Scaling from Trees to Forests: Analysis of a Complex Simulation Model. In: Science Online: An electronic journal (1997) (http://www.sciencemag.org)
Drogoul A., Corbara B., and Fresneau D.: MANTA: New experimental results on the emergence of (artificial) ant societies. In: Gilbert N. and Conte R. (eds.), Artificial Societies: the computer simulation of social life, UCL Press (1995)
El hadouaj S., Drogoul A., and Espié S.: To combine reactivity and anticipation:the case of conflicts resolution in a simulated road traffic. This volume.
Finin T., Fritzson R., and McKay D., et al.: An Overview of KQML: A Knowledge Query and Manipulation Language. Technical report, Department of Computer Science, University of Maryland, Baltimore County, USA (1992)
Gilbert N.: Computer Simulation of Social Processes. Social Research Update,Issue 6, Department of Sociology, University of Surrey, UK (1994) (http://www.soc.surrey.ac.uk/sru/)
Gilbert N. and Conte R. (eds.): Artificial Societies: the computer simulation of social life. UCL Press (1995)
Harding A. (ed.): Microsimulation and Public Policy. Elsevier (1996)
Harter A. and Hopper A.: A Distributed Location System for the Active Office.In: IEEE Network 8(1) (1994)
Hämäläsinen R.P.: Agent-Based Modeling of the Electricity Distribution System,In: IASTED International Conference on Modelling, Identification and Control,(1996) 344–346
Incropera F.P. and Witt D.P.: Fundamentals of Heat and Mass Transfer (3rd edition). Wiley and Sons (1990)
McCabe F.G. and Clark K.L.: April: Agent Process Interaction Language. In Wooldridge M. J. and Jennings N. R. (eds.), Intelligent Agents, Springer Verlag (1995) 324–340
Misra J.: Distributed Discrete-Event Simulation. ACM Computing Surveys, 18(sn1) (1986) 39–65
Moss S., Pahl-Wostl C., and T. Downing: Agent Based Integrated Assessment Modelling: The example of Climate Change, Integrated Assessment, 1, 2000.
Ogata K.: Modern Control Engineering (2nd edition). Prentice-Hall (1990)
Parunak H.V.D., Savit R., Riolo R.L.: Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users’ Guide. In Sichman, J.S., Conte, R.,Gilbert, N. (eds.), Multi-Agent Systems and Agent-Based Simulation, Springer Verlag (1998) 10–26
Reynolds C.W.: Flocks, Herds, and Schools: A Distributed Behavioral Model”, Computer Graphics, 21(4) (1987) 25–34
Williams R.J., “Simulation for public order training and preplanning”. In Kanecki D. (ed.), Emergency Management Conference (1994) 61–66
Yang, Q.: A Simulation Laboratory for Evaluation of Dynamic Traffic Management Systems. Ph.D. Thesis, Center for Transportation Studies, Massachusetts Institute of Technology, USA (1997)
Zeigler B.P.: Object Oriented Simulation with Hierarchical Modular Models.Academic Press (1990)
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Davidsson, P. (2000). Multi Agent Based Simulation: Beyond Social Simulation. In: Moss, S., Davidsson, P. (eds) Multi-Agent-Based Simulation. MABS 2000. Lecture Notes in Computer Science(), vol 1979. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44561-7_7
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DOI: https://doi.org/10.1007/3-540-44561-7_7
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