Computational Complexity

2012 Edition
| Editors: Robert A. Meyers (Editor-in-Chief)

Artificial Intelligence in Modeling and Simulation

  • Bernard Zeigler
  • Alexandre Muzy
  • Levent Yilmaz
Reference work entry

Article Outline


Definition of the Subject


Review of System Theory and Framework for Modeling and Simulation

Fundamental Problems in M&S

AI-Related Software Background

AI Methods in Fundamental Problems of M&S

Automation of M&S

SES/Model Base Architecturel for an Automated Modeler/Simulationist

Intelligent Agents in Simulation

Future Directions



Unify Modeling Language Intelligent Agent Round Trip Time Source System Activity Tracking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.


Primary Literature

  1. 1.
    Wymore AW (1993) Model-based systems engineering: An introduction to the mathematical theory of discrete systems and to the tricotyledon theory of system design. CRC, Boca RatonGoogle Scholar
  2. 2.
    Zeigler BP, Kim TG, Praehofer H (2000) Theory of modeling and simulation. Academic Press, New YorkGoogle Scholar
  3. 3.
    Ören TI, Zeigler BP (1979) Concepts for advanced simulation methodologies. Simulation 32(3):69–82Google Scholar
  4. 4.
  5. 5.
    Knepell PL, Aragno DC (1993) Simulation validation: a confidence assessment methodology.IEEE Computer Society Press, Los AlamitosGoogle Scholar
  6. 6.
    Law AM, Kelton WD (1999) Simulation modeling and analysis, 3rd edn. McGraw-Hill, ColumbusGoogle Scholar
  7. 7.
    Sargent RG (1994) Verification and validation of simulation models.In: Winter simulation conference. pp 77–84Google Scholar
  8. 8.
    Balci O (1998) Verification, validation, and testing.In: Winter simulation conference.Google Scholar
  9. 9.
    Davis KP, Anderson AR (2003) Improving the composability of department of defense models and simulations, RAND technical report. Accessed Nov 2007; J Def Model Simul Appl Methodol Technol 1(1):5–17
  10. 10.
    Ylmaz L, Oren TI (2004) A conceptual model for reusable simulations within a model-simulator-context framework.Conference on conceptual modeling and simulation.Conceptual Models Conference, Italy, 28–31 October, pp 28–31Google Scholar
  11. 11.
    Traore M, Muxy A (2004) Capturing the dual relationship between simulation models and their context.Simulation practice and theory. ElsevierGoogle Scholar
  12. 12.
    Page E, Opper J (1999) Observations on the complexity of composable simulation.In: Proceedings of winter simulation conference, Orlando, pp 553–560Google Scholar
  13. 13.
    Kasputis S, Ng H (2000) Composable simulations. In: Proceedings of winter simulation conference, Orlando, pp 1577–1584Google Scholar
  14. 14.
    Sarjoughain HS (2006) Model composability. In: Perrone LF, Wieland FP, Liu J, Lawson BG, Nicol DM, Fujimoto RM (eds) Proceedings of the winter simulation conference, pp 104–158 Google Scholar
  15. 15.
    DiMario MJ (2006) System of systems interoperability types and characteristics in joint command and control. In: Proceedings of the 2006 IEEE/SMC international conference on system of systems engineering, Los Angeles, April 2006Google Scholar
  16. 16.
    Sage AP, Cuppan CD (2001) On the systems engineering and management of systems of systems and federation of systems.Information knowledge systems management, vol 2, pp 325–345Google Scholar
  17. 17.
    Dahmann JS, Kuhl F, Weatherly R (1998) Standards for simulation: as simple as possible but not simpler the high level architecture for simulation.Simulation 71(6):378zbMATHCrossRefGoogle Scholar
  18. 18.
    Sarjoughian HS, Zeigler BP (2000) DEVS and HLA: Complimentary paradigms for M&S?Trans SCS 4(17):187–197Google Scholar
  19. 19.
    Yilmaz L (2004) On the need for contextualized introspective simulation models to improve reuse and composability of defense simulations.J Def Model Simul 1(3):135–145MathSciNetGoogle Scholar
  20. 20.
    Tolk A, Muguira JA (2003) The levels of conceptual interoperability model (LCIM).In: Proceedings fall simulation interoperability workshop, Accessed Aug 2008
  21. 21.
  22. 22.
  23. 23.
  24. 24.
  25. 25.
  26. 26.
  27. 27.
  28. 28.
    Object Modeling Group (OMG)
  29. 29.
  30. 30.
    Zeigler BP (1990) Object Oriented Simulation with Hierarchical, Modular Models: Intelligent Agents and Endomorphic Systems. Academic Press, OrlandozbMATHGoogle Scholar
  31. 31.
  32. 32.
    Mittal S, Mak E, Nutaro JJ (2006) DEVS-Based dynamic modeling & simulation reconfiguration using enhanced DoDAF design process. Special issue on DoDAF.J Def Model Simul, Dec (3)4:239–267Google Scholar
  33. 33.
    Ziegler BP (1988) Simulation methodology/model manipulation.In: Encyclopedia of systems and controls. Pergamon Press, EnglandGoogle Scholar
  34. 34.
    Alexiev V, Breu M, de Bruijn J, Fensel D, Lara R, Lausen H (2005) Information integration with ontologies.Wiley, New YorkGoogle Scholar
  35. 35.
    Kim L (2003) Official XMLSPY handbook.Wiley, IndianapolisGoogle Scholar
  36. 36.
    Zeigler BP, Hammonds P (2007) Modeling & simulation-based data engineering: introducing pragmatics into ontologies for net-centric information exchange.Academic Press, New YorkGoogle Scholar
  37. 37.
    Simard RJ, Zeigler BP, Couretas JN (1994) Verb phrase model specification via system entity structures. AI and Planning in high autonomy systems, 1994. Distributed interactive simulation environments. Proceedings of the Fifth Annual Conference, 7–9 Dec 1994, pp 192–1989Google Scholar
  38. 38.
    Checkland P (1999) Soft systems methodology in action.Wiley, London Google Scholar
  39. 39.
    Holland JH (1992) Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, CambridgeGoogle Scholar
  40. 40.
    Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning.Addison-Wesley Professional, PrincetonzbMATHGoogle Scholar
  41. 41.
    Davis L (1987) Genetic algorithms and simulated annealing.Morgan Kaufmann, San FranciscozbMATHGoogle Scholar
  42. 42.
    Zbigniew M (1996) Genetic algorithms + data structures = evolution programs. Springer, HeidelbergzbMATHGoogle Scholar
  43. 43.
    Cheon S (2007) Experimental frame structuring for automated model construction: application to simulated weather generation.Doct Diss, Dept of ECE, University of Arizona, TucsonGoogle Scholar
  44. 44.
    Zeigler BP (1984) Multifaceted modelling and discrete event simulation. Academic Press, LondonGoogle Scholar
  45. 45.
    Rozenblit JW, Hu J, Zeigler BP, Kim TG (1990) Knowledge-based design and simulation environment (KBDSE): foundational concepts and implementation.J Oper Res Soc 41(6):475–489Google Scholar
  46. 46.
    Kim TG, Lee C, Zeigler BP, Christensen ER (1990) System entity structuring and model base management.IEEE Trans Syst Man Cyber 20(5):1013–1024Google Scholar
  47. 47.
    Zeigler BP, Zhang G (1989) The system entity structure: knowledge representation for simulation modeling and design.In: Widman LA, Loparo KA, Nielsen N (eds) Artificial intelligence, simulation and modeling.Wiley, New York, pp 47–73Google Scholar
  48. 48.
    Luh C, Zeigler BP (1991) Model base management for multifaceted systems. ACM Trans Model Comp Sim 1(3):195–218zbMATHCrossRefGoogle Scholar
  49. 49.
    Couretas J (1998) System entity structure alternatives enumeration environment (SEAS).Doctoral Dissertation Dept of ECE, University of ArizonaGoogle Scholar
  50. 50.
    Hyu C Park, Tag G Kim (1998) A relational algebraic framework for VHDL models management.Trans SCS 15(2):43–55Google Scholar
  51. 51.
    Chi SD, Lee J, Kim Y (1997) Using the SES/MB framework to analyze traffic flow.Trans SCS 14(4):211–221Google Scholar
  52. 52.
    Cho TH, Zeigler BP, Rozenblit JW (1996) A knowledge based simulation environment for hierarchical flexible manufacturing.IEEE Trans Syst Man Cyber- Part A: Syst Hum 26(1):81–91Google Scholar
  53. 53.
    Carruthers P (2006) Massively modular mind architecture the architecture of the mind.Oxford University Press, USA, pp 480Google Scholar
  54. 54.
    Wolpert L (2004) Six impossible things before breakfast: The evolutionary origin of belief, W.W. Norton LondonGoogle Scholar
  55. 55.
    Zeigler BP (2005) Discrete event abstraction: an emerging paradigm for modeling complex adaptive systems perspectives on adaptation, In: Booker L (ed) Natural and artificial systems, essays in honor of John Holland.Oxford University Press, OxfordGoogle Scholar
  56. 56.
    Nutaro J, Zeigler BP (2007) On the stability and performance of discrete event methods for simulating continuous systems.J Comput Phys 227(1):797–819MathSciNetzbMATHCrossRefGoogle Scholar
  57. 57.
    Muzy A, Nutaro JJ (2005) Algorithms for efficient implementation of the DEVS & DSDEVS abstract simulators. In: 1st Open International Conference on Modeling and Simulation (OICMS).Clermont-Ferrand, France, pp 273–279Google Scholar
  58. 58.
    Muzy A The activity paradigm for modeling and simulation of complex systems.(in process)Google Scholar
  59. 59.
    Hofstadter D (2007) I am a strange loop.Basic BooksGoogle Scholar
  60. 60.
    Minsky M (1988) Society of mind. Simon & Schuster, GoldmanGoogle Scholar
  61. 61.
    Alvin I (2006) Goldman simulating minds: the philosophy, psychology, and neuroscience of mindreading.Oxford University Press, USAGoogle Scholar
  62. 62.
    Denning PJ (2007) Computing is a natural science.Commun ACM 50(7):13–18CrossRefGoogle Scholar
  63. 63.
    Luck M, McBurney P, Preist C (2003) Agent technology: enabling next generation computing a roadmap for agent based computing. Agentlink, LiverpoolGoogle Scholar
  64. 64.
    Miller JH, Page SE (2007) Complex adaptive systems: an introduction to computational models of social life.Princeton University Press, PrincetonzbMATHGoogle Scholar
  65. 65.
    Ferber J (1999) Multi-Agent systems: an introduction to distributed artificial intelligence.Addison-Wesley, PrincetonGoogle Scholar
  66. 66.
    Gasser L, Braganza C, Herman N (1987) Mace: a extensible testbed for distributed AI research.Distributed artificial intelligence – research notes in artificial intelligence, pp 119–152Google Scholar
  67. 67.
    Agha G, Hewitt C (1985) Concurrent programming using actors: exploiting large-scale parallelism. In: Proceedings of the foundations of software technology and theoretical computer science, Fifth Conference, pp 19–41Google Scholar
  68. 68.
    Smith RG (1980) The contract net protocol: high-level communication and control in a distributed problem solver.IEEE Trans Comput 29(12):1104–1113CrossRefGoogle Scholar
  69. 69.
    Shoham Y (1993) Agent-oriented programming.Artif Intell 60(1):51–92MathSciNetCrossRefGoogle Scholar
  70. 70.
    Dahl OJ, Nygaard K (1967) SIMULA67 Common base definiton.Norweigan Computing Center, NorwayGoogle Scholar
  71. 71.
    Rao AS, George MP (1995) BDI-agents: from theory to practice.In: Proceedings of the first intl.conference on multiagent systems, San FranciscoGoogle Scholar
  72. 72.
    Firby RJ (1992) Building symbolic primitives with continuous control routines.In: Procedings of the First Int Conf on AI Planning Systems.College Park, MD pp 62–29Google Scholar
  73. 73.
    Yilmaz L, Paspuletti S (2005) Toward a meta-level framework for agent-supported interoperation of defense simulations.J Def Model Simul 2(3):161–175Google Scholar

Books and Reviews

  1. 74.
    Alexiev V, Breu M, de Bruijn J, Fensel D, Lara R, Lausen H (2005) Information integration with ontologies. Wiley, New YorkGoogle Scholar
  2. 75.
    Alvin I (2006) Goldman Simulating Minds: The philosophy, psychology, and neuroscience of mindreading.Oxford University Press, USAGoogle Scholar
  3. 76.
    Carruthers P (2006) Massively modular mind architecture The architecture of the mind.
  4. 77.
    John H (1992) Holland adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. The MIT Press CambridgeGoogle Scholar
  5. 78.
    Zeigler BP (1990) Object oriented simulation with hierarchical, modular models: intelligent agents and endomorphic systems.Academic Press, OrlandozbMATHGoogle Scholar
  6. 79.
    Zeigler BP, Hammonds P (2007) Modeling & simulation-based data engineering: introducing pragmatics into ontologies for net-centric information exchange.Academic Press, New YorkGoogle Scholar
  7. 80.
    Zeigler BP, Kim TG, Praehofer H (2000) Theory of modeling and simulation. Academic Press, New YorkGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Bernard Zeigler
    • 1
  • Alexandre Muzy
    • 2
  • Levent Yilmaz
    • 3
  1. 1.Arizona Center for Integrative Modeling and SimulationUniversity of ArizonaTucsonUSA
  2. 2.CNRSUniversità di CorsicaCorteFrance
  3. 3.Auburn UniversityAlabamaUSA