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Artificial intelligence in simulation

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Abstract

The aims are to clarify some basic concepts on cognizant simulation as well as on the similarity and the relationships of AI and simulation; provide an inventory and a taxonomy of AI-based (cognizant) simulation and AI-assisted simulation (cognizant simulation environments); and to indicate some desirable research directions.

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References

  1. B.A. Abdulmajid and R.J. Wynne, Embedded qualitative simulation for process control,Proc. Summer Computer Simulation Con. (1991) pp. 369–374.

  2. S.V. Ahamed and E.G. Roman, A model-based expert system for decision support in negotiating, in:Modelling and Simulation Methodology in the Artificial Intelligence Era, ed. M.S. Elzas, T.I. Ören and B.P. Zeigler (North-Holland, Amsterdam, 1986) pp. 339–352.

    Google Scholar 

  3. E. Aldo and V. Mario, A Prolog simulator for FMS design and management,Proc. Al and Simulation, Simulation Series 22 SCSI, San Diego, CA, 1990) pp. 41–46.

  4. P.A. Anderson and S.J. Thorson, Systems simulatior — Artificial intelligence based simulations of foreign policy decision making, Behav. Sci. 27(1982)176–193.

    Google Scholar 

  5. P.L. Andrews and D.J. Latham, BEHAVE: A knowledge-based expert system for predicting wildland fire behavior,Proc. Summer Computer Simulation Con. (1984) pp. 1213–1218.

  6. H. de S. Arons, Real-time expert scheduling in manufacturing,Proc. European Simulation Symp. (1990) pp. 124–127.

  7. J.S. Aronson, AES: An object-oriented, knowledge-based approach to simulation,Proc. Summer Computer Simulation Conf. (1992), pp. 375–379.

  8. Artificial Intelligence Ltd.,STEM User Guide (Artificial Intelligence Ltd., Watford, England, 1988).

    Google Scholar 

  9. W.M. Austin and B. Khoshnevis, Qualitative modeling using natural language: An application in system dynamics, in:Qualitative Simulation Modeling and Analysis, ed. P.A. Fishwick and P.A. Luker (Springer, New York, 1991) pp. 263–301.

    Google Scholar 

  10. K.Z. Aytaç and T.I. Ören, MAGEST: A model-based advisor and certifier for GEST programs, in:Modelling and Simulation Methodology Artificial Intelligence Era, ed. M.S. Elzas, T.I. Ören and B.P. Zeigler (North-Holland, Amsterdam, 1986) pp. 299–307.

    Google Scholar 

  11. O. Balci, Guidelines for successful simulation studies,Proc. Winter Simulation Conf. (1991) pp. 25–32.

  12. V. Baskaran, M. Fox, N. Sathi and J. Bouer, SIMULATION CRAFT: An artificial intelligence approach to simulation model creation,Proc. IASTED Conf. (1986).

  13. H.W. Beck and P.A. Fishwick, Incorporating natural language descriptions into modelling and simulation, Technical Report TR-88-6, Univ. of Florida, Dept. of Computer and Information Sciences, Gainsville, FL (1988).

    Google Scholar 

  14. H.W. Beck and P.A. Fishwick, Natural language, cognitive models and simulation, in:Qualitative Simulation Modeling and Analysis, ed. P.A. Fishwick and P.A. Luker (Springer, New York, 1991) pp. 302–325.

    Google Scholar 

  15. D. Ben-Arieh, A knowledge-based system for simulation and control of a CIM,Proc. 2nd Int. Conf. on Simulation in Manufacturing (1986) pp. 13–21.

  16. J.A. Benavides and D.L. Wyatt, Improving digital circuit simulation: A knowledge based approach,Proc. Summer Computer Simulation Conf. (1988) pp. 364–371.

  17. S. Bennett, M.T. Rahbar, D.A. Linkens, E. Tany and M. Smith, Knowledge-based environment for modelling and simulation (KEMS),Proc. SCS Western Multiconf., Simulation Series, 20:3 (SCSI, San Diego, CA, 1989) pp. 62–651

    Google Scholar 

  18. R. Berenson, Wrapping AI techniques around simulation and other operations research tools to help manufacturing managers,Proc. First AAAI Workshop on Artificial Intelligence and Simulation (1986) Article No. 59.

  19. J.E. Biegel and R.V. Rogers, Intelligent simulations,Proc. Summer Computer Simulation Conf. (1990) pp. 653–659.

  20. G.K. Blackwell, J. Rangachari and C.T. Stockel, An intelligent interactive environment for a maritime real-time expert system,Proc. Summer Computer Simulation Conf. (1991) pp. 358–363.

  21. W.E. Blaisdel and J. Haddock, SIMSTAT: A tool for simulation analysis,Proc. Winter Simulation Conf. (1992) pp. 421–425.

  22. J.W. Blakemore, S.B. Dolins and P.R. Thrift, A general purpose robotic vehicle simulator, in:AI Applied to Simulation, ed. E.J.H. Kerckhoffs, G.C. Vansteenkiste and B.P. Zeigler, Simulation Series 18:1 (SCSI, San Diego, CA, 1986) pp. 151–161

    Google Scholar 

  23. D.G. Bobrow,Qualitative Reasoning About Physical Systems (MIT Press, Cambridge, MA, 1985).

    Google Scholar 

  24. J.H. Bradley, Artificial intelligence in simulators: Intention, implementation, falcification and contingency, Simulators VIII Simulation Series 24:1 (SCSI, San Diego, CA, 1991).

    Google Scholar 

  25. D. Breugnot, M. Gourgand and P. Kellert, SIGMA: An intelligent and graphical system for the modelling of assembly systems,Proc. European Simulation Symp. (1990) pp. 225–230.

  26. T. Brown et al., Demonstration of an expert system for manufacturing process control, in:AI, Graphics and Simulation, ed. G. Birtwistle (SCSI, San Diego, CA, 1985) pp. 110–113.

    Google Scholar 

  27. J. Byleckie, The application of simulation to the validation of knowledge-based systems,Proc. Summer Computer Simulation Conf. (1993) pp. 228–231.

  28. R.A. Campbell, Development of an expert system for simulation model selection,Proc. Intelligent Simulation Environments, Simulation Series 17:1 (SCSI, San Diego, CA, 1986) pp. 121–122.

    Google Scholar 

  29. J.G. Carbonell, Introduction: Paradigms for machine learning, Art. Int. 40(1989)1–9.

    Google Scholar 

  30. F.E. Cellier, Qualitative modeling and simulation: Promise and illusion,Proc. Winter Simulation Conf. (1991) pp. 1086–1090.

  31. Y. Cheng, Rule-based train traffic rearrangement interactive simulator,Proc. 2nd Beijing Int. Conf. on System Simulation and Scientific Computing (1992) pp. 681–685.

  32. Y. Cheng and S. Hu, Simulating liquor-tasting expert by neural computer,Proc. 2nd Beijing Int. Conf. on System Simulation and Scientific Computing (1992) pp. 4–6.

  33. K. Cho, K. Kim and H. Cho, An intelligent environment for computer system design,Proc. AI and Simulation, Simulation Series 22:3 (SCSI, San Diego, CA, 1990) pp. 59–64.

    Google Scholar 

  34. J. Cleary, K.-S. Goh and B. Unger, Discrete event simulation in Prolog,Proc. of Artificial Intelligence, Graphics and Simulation (1985) pp. 8–13.

  35. J.K. Clema and M.S. Fynewever, The simulation of of human learning and human decision making,Proc. 6th Annual Simulation Symp. (1973) pp. 96–115.

  36. J.R. Clymer, Simulation and design of artificially intelligent/adaptive decision making in systems, Proc. Artificial Intelligence and Simulation, Simulation Series 23:4 (SCSI, San Diego, CA, 1991) pp. 83–92.

    Google Scholar 

  37. P.E. Coats, Combining an expert system with simulation to enhance planning for banking networks, Simulation 50(1990)253–264.

    Google Scholar 

  38. J.K. Cochran, G.T. Mackulak, D. Castillo and E. Du, Configuring available software into an AI/ES environment for automated manufacturing simulation design on the PC,Proc. Simulation of Computer Integrated Manufacturing Systems and Robotics, San Diego, CA (1987).

  39. A.G. Cohn,Proc. 5th Conf. of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (Pitman, London, England; and Morgan Kaufmann, Palo Alto, CA, 1989).

    Google Scholar 

  40. D. Comis, When Comax speaks, farmers listen, Agricult. Res.(1986)6–10.

  41. P.J. Davis and R. Hersh,Descartes' Dream — The World According to Mathematics (Harcourt Brace Jovanovich, San Diego, CA, 1986).

    Google Scholar 

  42. P.K. Davis, Applying artificial intelligence techniques to strategic-level gaming and simulation, in:Modelling and Sinullation Methodology in the Artificial Intelligence Era, ed. M.S. Elzas, T.I. Ören and B.P. Zeigler (North-Holland, Amsterdam, 1986) pp. 315–338.

    Google Scholar 

  43. R. De Mori and R. Prager, Verification of flight simulators using a knowledge based system,Proc. European Simulation Multiconf. (1988) pp. 403–409.

  44. D. Deng and J.O. Jenkins, Artificial intelligence validation of simulation models, Proc. AI and Simulation, Simulation Series 20:4 (SCSI, San Diego, CA, 1989) pp. 80–84.

    Google Scholar 

  45. V. Deslandres and H. Pierreval, An expert system prototype assisting the statistical validation of simulation models, Simulation 56(1991)79–89.

    Google Scholar 

  46. T. Deutch, I. Futo and I. Papp, The use of TC-Prolog for medical simulation,Proc. of Intelligent Simulation Environments (1986) pp. 299–334.

  47. G.I. Doukidis and R.J. Paul, Experiences in automating the formulation of discrete event simulation models, in:AI Applied to Simulation, ed. E.J.H. Kerckhoffs, G.C. Vansteenkiste and B.P. Zeigler, Simulation Series 18:1 (SCSI, San Diego, CA, 1986) pp 79–90.

    Google Scholar 

  48. G.I. Doukidis and R.J. Paul, SIPDES: A simulation program debugger using an expert system, Expert Syst. Appl. 2(1992)153–165.

    Google Scholar 

  49. M. Draman, TTESS: A tutorial tool for expert supervised simulations, Ph.D. Thesis, Univ. of Central Florida (1990).

  50. A.-M. Dubois, A draft of a component model library manager, The “MODELOTHEQUE”: Analysis of an approach and first results, in:Systems Analysis and Simulation II — Applications, ed. A. Sydow, S.G. Tzafestas and R. Vichnevetsky (Springer, New York, 1988) pp. 396–403.

    Google Scholar 

  51. M. Eisenberg, Combining qualitative and quantative techniques in the simulation of chemical reaction mechanisms,Proc. AI and Simulation, Simulation Series 22.3 (SCSI, San Diego, CA, 1990) pp. 233–242.

    Google Scholar 

  52. T. Ellman and J. Keane, Intelligent model selection for hillclimbing search in computer-aided design, in:Design from Principles, Papers from the 1992 Fall Symp. (AAAI Press, Menlo Park, CA, 1992) pp. 119–124.

    Google Scholar 

  53. A.S. Elmaghraby and V. Jagannathan, An expert system for simulationists — Problems, experiences, lessons, in:AI, Graphics and Simulation, ed. G. Birtwistle (SCSI, San Diego, CA, 1985) pp. 106–109.

    Google Scholar 

  54. M.S. Elzas, Artificial Intelligence and modelling of societal systems: A synopsis, in:Modelling and Simulation Methodology in the Artificial Intelligence Era, ed. M.S. Elzas, T.I. Ören and B.P. Zeigler (North-Holland, Amsterdam, 1986) pp. 311–314).

    Google Scholar 

  55. M.S. Elzas, T.I. Ören and B.P. Zeigler (eds.),Modelling and Simulation Methodology in the Artificial Intelligence Era (North-Holland, Amsterdam, 1986).

    Google Scholar 

  56. M.S. Elzas, T.I. Ören and B.P. Zeigler (eds.),Modelling and Simulation Methodology: Knowledge Systems' Paradigms (North-Holland, Amsterdam, 1989).

    Google Scholar 

  57. S.A. Epstein, RTSE: The reactor trip simulation environment,Proc. 1st. AAAI Workshop on Artificial Intelligence and Simulation (AAAI, Menlo Park, CA, 1986), Article No. 51.

    Google Scholar 

  58. S.A. Erickson, Jr., Fusing AI and simulation in military modelling, in:AI Applied to Simulation, ed. E.J.H. Kerckhoffs, G.C. Vansteenkiste and B.P. Zeigler, Simulation Series 18:1 (SCSI, San Diego, CA, 1986) pp. 140–150.

    Google Scholar 

  59. J. Esakov and N.I. Badler, An architecture for high-level human task animation control, in:Knowledge-Based Simulation: Methodology and Application, ed. P.A. Fishwick and R.B. Modjeski (Springer, New York, 1991) pp. 162–199.

    Google Scholar 

  60. A. Esposito and M. Vento, A Prolog simulator for FMS design and management,Proc. AI and Simulation, Simulation Series 22:3 (SCSI, San Diego, CA, 1990) pp. 41–46.

    Google Scholar 

  61. B.G. Farley and W.A. Clark, Simulation of self-organizing systems by digital computer, Trans. IRE PGIT-4(1954)76–84.

    Google Scholar 

  62. S. Feng, A simulation model-based Intelligent Economic Decision Support System (IEDSS),Proc. 2nd. Beijing Int. Conf. on System Simulation and Scientific Computing (1992) pp. 295–298.

  63. S. Feng and S. Deng, ES technology in Model-based Decision Support (MBDS),Proc. 2nd Beijing Int. Conf. on System Simulation and Scientific Computing (1992) pp. 304–308.

  64. N.V. Findler and N.M. Mazur, A system for automatic model verification and validation, Trans. Comp. Sim. 6(1990)153–172.

    Google Scholar 

  65. P.A. Fishwick, A study of terminology and issues in qualitative simulation, Simulation 52(1989)5–9.

    Google Scholar 

  66. P.A. Fishwick, A case study of natural language text generation for simulation analysis,Proc. AI and Simulation, Simulation Series 22:3 (SCSI, San Diego, CA, 1990) pp. 205–210.

    Google Scholar 

  67. P.A. Fishwick and P.A. Liker,Qualitative Simulation Modeling and Analysis (Springer, New York, 1991).

    Google Scholar 

  68. P.A. Fishwick and R.B. Modjeski (eds.),Knowledge Based Simulation: Methodology and Application (Springer, New York, 1991).

    Google Scholar 

  69. R.A. Fjelheim, A Knowledge-based interface to process simulation,AI Applied to Simulation, ed. E.J.H. Kerckhoffs, G.C. Vansteenkiste and B.P. Zeigler, Simulation Series 18:1 (SCSI, San Diego, CA, 1986) pp. 97–102.

    Google Scholar 

  70. K.D. Forbus and B. Falkenhainer, Self-explanatory simulations: An integration of qualitative and quantitative knowledge,Proc. 8th National Conf. on Artificial Intelligence (1991) pp. 380–387.

  71. L. Fortuna, A. Gallo, G. Nunnari and L. Occhipinti, Fast simulation of an induction motor using neural networks: An application to fault diagnosis,Proc. European Simulation Multiconf. (1993) pp. 235–239.

  72. M.S. Fox, N. Husain, M. McRoberts and Y.V. Reddy, Knowledge-based simulation: An artificial intelligence approach to system modeling and automating the simulation life cycle, in:Artificial Intelligence, Simulation and Modeling, ed. L.E. Widman, K.A. Loparo and N.R. Nielsen (Wiley, New York, 1989) pp. 447–486.

    Google Scholar 

  73. M.V. Frank and S.A. Epstein, Application of artificial intelligence to improve plant availability, in:Intelligent Simulation Environments, ed. P.A. Luker and H.H. Adelsberger, Simulation Series 17:1 (SCSCI, San Diego, CA, 1986) pp. 92–97.

    Google Scholar 

  74. L.-M. Fu, CAUSIM: A rule-based causal simulation system, Trans. Comp. Simul. 7(1990)251–264.

    Google Scholar 

  75. R. Fujiwara and T. Sakaguchi, An expert system for power system planning,Proc. European Conf., Simulation Series 18:1 (SCSI, San Diego, CA, 1986) pp. 174–177.

    Google Scholar 

  76. I. Futo and T. Gergely, TS-PROLOG: A logic simulation language, Trans. Soc. Comp. Simul. 3(1987)319–335.

    Google Scholar 

  77. B.R. Gaines, Expert systems and simulation in industrial applications,Proc. Intelligent Simulation Environments, Simulation Series 17:1 (SCSI, San Diego, CA, 1986) pp. 144–149.

    Google Scholar 

  78. H. Gallaire, A knowledge-based modelling and simulation tool, in:Modelling and Simulation Methodology: Knowledge Systems' Paradigms, ed. M.S. Elzas, T.I. Ören and B.P. Zeigler (North-Holland, Amsterdam, 1989) pp. 209–219.

    Google Scholar 

  79. C-B. Gao, A novel intelligent modelling software on Turbo-Prolog,Proc. Beijing Int. Conf. on System Simulation and Scientific Computing, Vol. 1 (1989) pp. 496–499.

    Google Scholar 

  80. P. Garrido and J. Neves, A control systems simulation methodology in concurrent Prolog,Proc. European Simulation Symp. (1990) pp. 21–25.

  81. A. Gelsey, From CAD/CAM to simulation: Automatic model generation for mechanical devices, in:Knowledge Based Simulation: Methodology and Application, ed. P.A. Fishwick and R.B. Modjeski (Springer, New York, 1991) pp. 108–132.

    Google Scholar 

  82. A. Gerstenfeld and Y. Pan, Qualitative simulation: A focus on air traffic control,Proc. 1st AAAI Workshop on Artificial Intelligence and Simulation (1986) Article No. 1.

  83. J. Glicksman, A simulator environment for an autonomous land vehicle, in:Intelligent Simulation Environment, ed. P.A. Luker and H.H. Adelsberger, Simulation Series 17:1 (SCSI, San Diego, CA, 1986) pp. 53–57.

    Google Scholar 

  84. A.J. Gonzales and D.D. Dankel,The Engineering of Knowledge-Based Systems: Theory and Practice, (Prentic-Hall, Englewood Cliffs, NJ, 1993).

    Google Scholar 

  85. I.J. Good,Speculation Concerning the First Ultra-Intelligent Machine (Institute for Defence Analyses, Princeton, NJ, 1964).

    Google Scholar 

  86. P. Gray and I. Borovits, The contrasting roles of Monte Carlo simulation and gaming in decision support systems, Simulation 47(1986)233–239.

    Google Scholar 

  87. A. Groen et al., The integration of simulation and knowledge-based systems, in:AI Applied to Simulation, ed. E.J.H. Kerckhoffs, G.C. Vansteenkiste and B.P. Zeigler, Simulation Series 18:1 (SCSI, San Diego, CA, 1986) pp. 189–197.

    Google Scholar 

  88. J. Grundspenkis, The use of knowledge based approach for simulation of fault propagation in complex technical systems,Proc. European Simulation Multiconf. (1993) pp. 217–221.

  89. G.H. Gunsch and B.V. Herbert, A proposed military planning task simulator using the ROSS language, M.S. Thesis, AFIT/GE/EE/83D-24, Air Force Institute of Technology, Wright-Patterson AFB, OH (1983).

  90. M.R. Halley, T. Miller, C. Hougum and W. Mosenthal, SONAR PLEXUS — Enhancing a command and control simulation with reasoning,Proc. Simulators Conf. (1987) pp. 15–18.

  91. R. Harre and R. Lamb (eds.),The Dictionary of Ethology and Animal Learning (MIT Press, Cambridge, MA, 1986).

    Google Scholar 

  92. D.H. Hellman and A. Bahuguna, Explanation systems for computer simulations,Proc. 1986 Winter Simulation Conf. (1986) pp. 453–459.

  93. J. Herczeg and M. Herczeg, A knowledge-based simulation for electronics circuits, Simulation Digest 19(1989).

  94. L.O. Hertzberger and F.C.A. Groen (eds.),Intelligent Autonomous Systems (North-Holland, Amsterdam, 1987).

    Google Scholar 

  95. G.W. Hopple, Artificial intelligence and simulation: An application to logistics modeling, in:Applied Artificial Intelligence: A Sourcebook, ed. S.J. Andriole and G.W. Hopple (McGraw-Hill, New York, 1992) pp. 283–297.

    Google Scholar 

  96. J. Hu and J.W. Rozenblit, Towards automatic generation of experimental frame in simulation-based system design,Proc. AI and Simulation, Simulation Series 20:1 (SCSI, San Diego, CA, 1988) pp. 1–6.

    Google Scholar 

  97. X.-X. Huang, Z.-l. Zhang and H. Li, Application of expert system in achievement grading for the training simulator,Proc. 2nd Beijing Int. Conf. on System Simulation and Scientific Computing (1992) pp. 280–284.

  98. Y. Iwasaki and B. Chandrasekaran, Design verification using functional knowledge, in:Papers from the 1992 AAAI Fall Symp. on Design from Principles, Technical Report FS-92-03 (AAAI Press, 1992) pp. 56–61.

  99. Y. Iwasaki and C.M. Low, Device modeling environment: An integrated model- formulation and simulation environment for continuous and discrete phenomena,Proc. 1st Int. Conf. on Intelligent Systems Engineering (1992).

  100. M. Izygon and C.L. Pitman, A knowledge-based front-end for a complex space flight simulation program, in:Proc. Summer Computer Simulation Conf. (1991) pp. 364–368.

  101. P.L. Jankowski, Knowledge-based structured modelling: An application to stream water quality management, Ph.D. Dissertation, University of Washington, Seattle (1989).

    Google Scholar 

  102. A. Jávor, Applications of expert systems concepts to adaptive experimentation with models, in:Modelling and Simulation Methodology in the Artificial Intelligence Era, ed. M.S. Elzas, T.I. Ören and B.P. Zeigler (North-Holland, Amsterdam, 1986) pp. 153–163.

    Google Scholar 

  103. A. Jávor, Demons in simulation: A novel approach, Syst. Anal. Mod. Simul. 7(1990)331–338.

    Google Scholar 

  104. A. Jávor, Intelligent demon controlled simulation of flexible manufacturing system,Proc. 2nd Beijing Int. Conf. on System Simulation and Scientific Computing (1992) pp. 333–337.

  105. J.W. Jones, Applying agricultural models using expert system concepts, Proc. Amer. Soc. Agricult. Eng. (1986).

  106. T.K. Joseph, An expert system advisor to aid goal definition for manufacturing system simulation,Proc. AI and Simulation, Simulation Series 20:4 (SCSI, San Diego, CA, 1989) pp. 203–208.

    Google Scholar 

  107. E.K. Juuso, J.K. Tenteris and K. Leiviskä, Structural models in combined simulation and expert system of ferroalloy processes,Proc. European Simulation Symp. (1990) pp. 33–37.

  108. V. Kachitvichyanakul, S.-W.J. Cheng and D.R. Denzler, KBSIM: A knowledge-based simulator for FMS scheduling and dispatching,Proc. Summer Computer Simulation Conf. (1986) pp. 696–698.

  109. K. Kawamura, Coupling symbolic and numerical computations (Spacecraft control expert system),IEEE Proc. Int. Conf. on Cybernetics and Society (1985) pp. 507–510.

  110. E.J.H. Kerckhoffs, A view on problem-solving paradigms including neurocomputing, Neural Network World (Int. J. on Neural and Mass-Parallel Computing and Information Systems, Prague, Czech Republic) 3/91(1991)129–154.

  111. E.J.H. Kerckhoffs, H. Koppelaar and G.C. Vansteenkiste, Integrating symbolic and neural computing,Proc. 2nd Beijing Int. Conf. on System Simulation and Scientific Computing (1992) pp. 1–7.

  112. E.J.H. Kerckhoffs, G.C. Vansteenkiste and B.P. Zeigler (eds.),Proc. European Conf. on AI Applied to Simulation, Simulation Series 18:1 (SCSI, San Diego, CA, 1985).

  113. L.C. Keskey and D.J. Skyes, An artificial intelligence (AI)-simulation based approach for aircraft maintenance training,Simulators Simulation Series 18:4 (SCSI, San Diego, CA, 1987) pp. 285–290.

    Google Scholar 

  114. A.M. Keuneke, Functional simulation of device: Causal explanation of diagnostic conclusions,Proc. Summer Computer Simulation Conf. (1990) pp. 709–714.

  115. B. Khoshnevis and A-P. Chen, An expert simulation model builder,Intelligent Simulation Environments, Simulation Series 17:1 (SCSI, San Diego, CA, 1986) pp. 129–132.

    Google Scholar 

  116. B. Khoshnevis and S. Parisay, Machine learning and simulation: Application in queueing systems, Simulation 61(1993)294–302.

    Google Scholar 

  117. M.-H. Kim, III-SE: An Integrated-Interactive-Intelligent Simulation Environment,Proc. 2nd Beijing Conf. on System Simulation and Scientific Computing (1992) pp. 95–98.

  118. T.G. Kim and B.P. Zeigler, ESP-scheme: A realization of system entity structure in a LISP environment,Proc. AI and Simulation, Simulation Series 20:4 (SCSI, San Diego, CA 1989) pp. 135–140.

    Google Scholar 

  119. J. Kitowski, Simulation of the THTR-steam generator control using artificial intelligence methods,Proc. IMACS-82, Vol. 2 (1983) pp. 67–69.

    Google Scholar 

  120. G.J. Klir,Aspects of Systems Science (Plenum, New York, 1991).

    Google Scholar 

  121. K.J. Kochut, J.A. Miller and W.D. Potter, Design of a CLOS version of active KDL: A knowledge/data base system capable of query driven simulation,Proc. Artificial Intelligence and Simulation, Simulation Series 23:4 (SCSI, San Diego, CA, 1991) pp. 139–145.

    Google Scholar 

  122. R. König and R. Langbein, PROROAD: simulation of the consequences of traffic management strategies on the road traffic,Proc. European Simulation Multiconf. (1993) pp. 481–485.

  123. R.M. Krumholz, CAN BUILD: A state-of-the need inventory simulation tool, in:Innovative Applications of Artificial Intelligence, ed. H. Schorr and A. Rappoport (AAAI Press, Menlo Park, CA, 1990) pp. 137–148.

    Google Scholar 

  124. B. Kuipers, The limits of qualitative simulation,Proc. 9th Int. Conf. on Artificial Intelligence (1990) pp. 128–136.

  125. B. Kuipers, Qualitative simulation, Art. Int. 29(1990)289–338.

    Google Scholar 

  126. N.N. Kwanjai and R.H. Wild, Knowledge-based simulation to assist in system design identification,Proc. Winter Simulation Conf. (1992) pp. 822–830.

  127. C.P. Langlotz, L.M. Fagan, S.W. Tu, J. Williams and B. Sikic, ONYX: An architecture for planning under uncertainty,Proc. 9th Int. Joint Conf. on Artificial Intelligence (1985) pp. 447–449.

  128. G.D. Lanier and B. Vizzier, ESDOTS: An object oriented knowledge based system for space vehicle processing analysis,Proc. Summer Computer Simulation Conf. (1991) pp. 380–385.

  129. A. Lehmann, Knowledge-based modelling and simulation: Restrictions, alternatives and applications, in:Systems Analysis and Simulation I — Theory and Foundations, ed. A. Sydow, S.G. Tzafestas and R. Vichnevetsky (Springer, New York, 1988) pp. 412–418.

    Google Scholar 

  130. B. Li and Q. Zhao, Analogical reasoning as a mechanism for generation of programs, in:Proc. 2nd Beijing Int. Conf. on System Simulation and Scientific Computing (1992) pp. 99–104.

  131. Q. Liang and R.-S. Tu, Intelligent software for simulation of social systems,Proc. Beijing Int. Conf. on System Simulation and Scientific Computing, Vol. 1 (1989) pp. 353–356.

    Google Scholar 

  132. R. Lippmann, An introduction to computing with neural nets, IEEE ASSP Mag. (1987)4–22.

  133. Y. Lirov, E.Y. Rodin, B.G. McElhany and L.W. Wilbur, Artificial intelligence modelling of control systems, Simulation 50(1988)12–24.

    Google Scholar 

  134. F. Liu and B. Shen, ESSMA — An expert system of simulation model building and algorithm selection,Proc. 2nd Beijing Int. Conf. on System Simulation and Scientific Computing (1992) pp. 309–313.

  135. K.-C. Liu and J.W. Rozenblit, Applying knowledge-based system design and simulation in information system requirements determination,Proc. Winter Simulation Conf. (1990) pp. 407–411.

  136. C. Loehle, Applying artificial intelligence techniques to ecological modelling, Ecol. Mod. 38(1987)191–212.

    Google Scholar 

  137. G.T. Mackulak and J.K. Cochran, MASCOT: A Prolog-based simulation modelling and training environment, in:Modelling and Simulation Methodology: Knowledge Systems' Paradigms, ed. M.S. Elzas, T.I. Ören and B.P. Zeigler (North-Holland, Amsterdam, 1987) pp. 145–159.

    Google Scholar 

  138. C.A. Marsh, RBMS — An expert system for modelling NASA flight control room usage,Artificial Intelligence and Simulation (SCSI, San Diego, CA, 1987) pp. 47–50.

    Google Scholar 

  139. K. Mayoram and D.O. Pederson, Circuit simulation in LISP,Proc. ICCAD-84 (1987) pp. 24–26.

  140. M.E. McFall and P. Klahr, Simulation with rules and objects,Proc. Winter Simulation Conf. (1986) pp. 470–473.

  141. J.M. McKinion, Modvex: An expert system for model validation, maintenance, coordination and development,Proc. 1st AAAI Workshop onArtificial Intelligence and Simulation (1986) Article No. 8.

  142. R.R. Meijer and E.J.H. Kerckhoffs, Towards the modelling of knowledge-based control systems in “Duties” (A parallel environment for coupled numeric/symbolic systems),Proc. European Simulation Symp. (1990) pp. 73–79.

  143. J.M. Mellichamp and Y.H. Park, A Statistical expert system for simulation analysis, Simulation 50(1989)134–139.

    Google Scholar 

  144. Y.A. Merkuryev and G.V. Merkuryeva, Expert simulation system for manufacturing processes,Proc. Summer Computer Simulation Conf. (1991) pp. 405–408.

  145. S. Middleton and R. Zanconato, BLOPS: An object-oriented language for simulation and reasoning, in:AI Applied to Simulation (1989) pp. 130–135.

  146. J.G. Moser, Integration of artificial intelligence and simulation in a comprehensive decision support system, Simulation 47(1986)223–229.

    Google Scholar 

  147. H.E. Mueller, L.J. Folse and Brown III, Simulation of animal learning using an inductive memory,Proc. Artificial Intelligence and Simulation, Simulation Series 23:4 (SCSI, San Diego, CA, 1991) pp. 104–108.

    Google Scholar 

  148. R. Muetzzelfeld, A. Bundy, M. Uschold and D. Robertson, ECO — An intelligent front end for ecological modelling, in:AI Applied to Simulation, Simulation Series 18:1 (SCSI, San Diego, CA, 67–70, 1986) pp. 73–78.

    Google Scholar 

  149. K.J. Murray, Knowledge-based model construction: An automatic programming approach to simulation modelling, Ph.D. Dissertation, Department. of Computer Science, Texas A&M University, College Station, TX (1986).

    Google Scholar 

  150. K.J. Murray and S.V. Sheppard, Knowledge-based simulation model specification, Simulation 50(1988)112–119.

    Google Scholar 

  151. G. Nadoli, D. Castillo and J.E. Biegel, HRS: A structure for hierarchical reasoning in knowledge based simulation,Proc. AI and Simulation, Simulation Series 22:3 (SCSI, San Diego, CA, 1990) pp. 34–40.

    Google Scholar 

  152. A. Newell and H.A. Simon, The simulation of human thought, in:Current Trends in Psychological Theory (University of Pittsburgh Press, Pittsburgh, 1961) pp. 152–169.

    Google Scholar 

  153. M.S. Obaidat and D.T. Macchiarolo, On-line neurocomputing system to identify computer users,Proc. Summer Computer Simulation Conf. (1992) pp. 403–407.

  154. R.M. O'Keefe, Advisory systems in simulation,Proc. AI Applied to Simulation, Simulation Series 18:1 (SCSI, San Diego, CA, 1986) pp. 73–78.

    Google Scholar 

  155. R.M. O'Keefe, Simulation and expert systems — A taxonomy and some examples, Simulation 46(1986)10–16.

    Google Scholar 

  156. R.M. O'Keefe, J. Roach and R. Iyengar, Artificial intelligence and simulation,Proc. 1st AAAI Workshop on Artificial Intelligence and Simulation (1986) Article No. 21.

  157. T.I. Ören, Model-based activities: A paradigm shift, in:Simulation and Model-Based Methodologies: An Integrative view, ed. T.I. Ören, B.P. Zeigler and M.S. Elzas (Sprinrer, Heidelberg, Germany, 1984) pp. 3–40.

    Google Scholar 

  158. T.I. Ören, Knowledge bases for an advanced simulation environment,Proc. Intelligent Simulation Environments, Simulation Series 17:1 (SCSI, San Diego, CA, 1986) pp. 13–22.

    Google Scholar 

  159. T.I. Ören, Implications of machine learning in simulation, in:Modelling and Simulation Methodology in the Artificial Intelligence Era, ed. M.S. Elzas, T.I. Ören and B.P. Zeigler (North-Holland, Amsterdam, 1986) pp. 41–57.

    Google Scholar 

  160. T.I. Ören, Quality assurance paradigms for artificial intelligence in modelling and simulation, Simulation 48(1987)149–151.

    Google Scholar 

  161. T.I. Ören, Three simulation experimentation environments: SimAd, SimGest and E/Slam,Proc. European Simulation Symp. (1993) pp. 627–632.

  162. T.I. Ören, D.G. King, L.G. Birta and M. Hitz, Requirements for a repository-based simulation environment,Proc. Winter Simulation Conf. (1992).

  163. T.I. Ören and G. Sheng, Semantic rules and facts for an expert modelling and simulation system,Proc. 12th IMACS World Congress, Vol. 2 (1988) pp. 596–598.

    Google Scholar 

  164. C.M. Oresky, D.B. Lenat, A. Clarkson and S.H. Kaisler, Strategic automatic discovery system (STRADS), in:Knowledge-Based Simulation: Methodology and Application, ed. P.A. Fishwick and R.B. Modjeski (Springer, New York, 1991) pp. 223–260.

    Google Scholar 

  165. C.M. Overstreet and R.E. Nance, World view based discrete event simplification, in:Modelling and Simulation Methodology in the Artificial Intelligence Era, ed. M.S. Elzas, T.I. Ören and B.P. Zeigler (North-Holland, Amsterdam, 1986) pp. 165–179.

    Google Scholar 

  166. M.L. Padgett and T.A. Roppel, Design of a neural network module for integration into a simulation,Proc. Summer Computer Simulation Conf. (1992) pp. 390–395.

  167. Dj.B. Petkovski, Knowledge-based systems for distributed decision making, in:Systems Analysis and Simulation I — Theory and Foundations, ed. A. Sydow, S.G. Tzafestas and R. Vichnevetsky (Springer, New York, 1988) pp. 406–411.

    Google Scholar 

  168. F. Pichler, Model components for symbolic processing by knowledge-based systems: The STIPS framework, in:Modelling and Simulation Methodology in the Artificial Intelligence Era, ed. M.S. Elzas, T.I. Ören and B.P. Zeigler (North-Holland, Amsterdam, 1986) pp. 133–143.

    Google Scholar 

  169. F. Pichlerand R.M. Díaz,Computer Aided Systems Theory — EUROCAST '91 (Springer, Berlin, 1991).

    Google Scholar 

  170. C.J. Puccia and R. Levins, Qualitative modeling in ecology: Loop analysis, signed digraphs and time averaging, in:Qualitative Simulation Modeling and Analysis, ed. P.A. Fishwick and P.A. Luker (Springer, New York, 1991) pp. 119–143.

    Google Scholar 

  171. A. Radiya and R.G. Sargent, Logic programming and discrete event simulation,Proc. Simulation and AI (SCS, San Diego, CA, 1987) pp. 64–71.

    Google Scholar 

  172. R.K. Ragade, A. Kumar and D.G. Strubel III, Simulation of intelligent operating systems,Proc. AI and Simulation, Simulation Series 22:3 (SCSI, San Diego, CA, 1990) pp. 188–193.

    Google Scholar 

  173. M.T. Rahbar, E. Tanyi, S. Bennett, D.A. Linkens and B. Thomson, An integrated database: Its role in a knowledge-based environment for modelling and simulation (KBMSE), in:Proc. 3rd European Simulation Congress (1989) pp. 187–192.

  174. V.S. Rajput and R.G.S. Asthana, An intelligent visual interactive simulator (IVIS) for planning and analysis of train services,Proc. European Simulation Symp. (1990) pp. 203–206.

  175. M. Rao, J. Tsung-shann, J.J.-p Tsai and C.K. Chang, An intelligent simulation environment for optimal control systems, in:Proc. Summer Computer Simulation Conf. (1987) pp. 842–844.

  176. S.M. Reddy, Knowledge-based techniques for process simulation,Proc. European Simulation Symp. (1990) pp. 80–82.

  177. S.M. Reddy, R. Turton, K.E. Williams, D.B. Godbole and S.M. Potnis, IPSE: A knowledge-based environment for process simulation,Proc. AI and Simulation, Simulation Series 20:4 (SCSI, San Diego, CA, 1989) pp. 185–188.

    Google Scholar 

  178. Y.V.R. Reddy, Epistemology of knowledge-based simulation, Simulation 48(1987)162–166.

    Google Scholar 

  179. Y.V.R. Reddy and M.S. Fox, KBS: An artificial intelligence approach to flexible simulation, Technical Report CMU-RI-TR-81-4, Carnegie Mellon University, Pittsburgh, PA (1980).

    Google Scholar 

  180. K.D. Reilly and V. Anumolu, Computer simulation of selective neural networks and character handling,Proc. Summer Computer Simulation Conf. (1991) pp. 349–351.

  181. K.D. Reilly, K.W. Ramer, P. Dey, B.W. Suter, H.E. Lyons and J. Byoun, A natural language component for a modeling and simulation environment, in:Simulation at the Frontiers of Science, ed. J. Joung, J.W. Ingalls and R. Hawkins (SCSI, San Diego, CA, 1986) pp. 83–88.

    Google Scholar 

  182. E. Rich and K. Knight,Artificial Intelligence, 2nd ed. (McGraw-Hill, New York, 1991).

    Google Scholar 

  183. J. Rothenberg, Artificial intelligence and simulation,Proc. Winter Simulation Conf. (1991) pp. 218–222.

  184. A. Round, Knowledge-based simulation, in:The Handbook of Artificial Intelligence, Vol. 4, ed. A. Barr, P.R. Cohen and E.A. Feigenbaum (Addison-Wesley, Reading, MA, 1989) pp. 415–518.

    Google Scholar 

  185. M. Rowland, M. Monk and M. Swabey, The simulation of aircrew behaviour for systems integration using knowledge-based programming,Proc. European Simulation Multiconf. (1993) pp. 459–463.

  186. J.W. Rozenblit, J.F. Hu, T.G. Kim and B.P. Zeigler, Knowledge-based design and simulation environment (KBDSE): Foundational concepts and implementation, J. Oper. Res. 41(1990)475–489.

    Google Scholar 

  187. J.W. Rozenblit, S. Sevinç and B.P. Zeigler, Knowledge-based design of LAN's using system entity structure concepts,Proc. Winter Simulation Conf. (1986) pp. 858–865.

  188. J.W. Rozenblit and B.P. Zeigler, Concepts of knowledge based system design environment,Proc. Winter Simulation Conf. (1985) pp. 223–231.

  189. S. Ruiz-Mier, J. Talavage and D. Ben-Arieh, Towards a knowledge-based network simulation environment,Proc. Winter Simulation Conf. (1985) pp. 232–236.

  190. S. Sakthivel and R. Agarwal, Knowledge-based model construction for simulating information systems, Simulation 59(1992)223–236.

    Google Scholar 

  191. R.R. Salgame, S.G. Becker and D.H. Yu, SPARKS: A knowledge-based process modeling and simulation system,Proc. Summer Computer Simulation Conf. (1990) pp. 1147–1156.

  192. A.J. Santiago and H. Chen, Traffic modeling software for IVHS application,Proc. Winter Simulation Conf. (1990) pp. 759–762.

  193. R.G. Sargent, Exploration of possibilities for expert aids in model validation, in:Modelling and Simulation Methodology in the Artificial Intelligence Era, ed. M.S. Elzas, T.I. Ören and B.P. Zeigler (North-Holland, Amsterdam, 1986) pp. 279–297.

    Google Scholar 

  194. N. Sathi, M. Fox, V. Baskaran and J. Bouer, SIMULATION CRAFT: An artificial intelligence approach to the simulation life cycle,Proc. Summer Computer Simulation Conf. (1986) pp. 773–778.

  195. L.P. Schnepf, A knowledge-based framework supporting the validation process of simulation models,Proc. European Simulation Symp. (1992) pp. 39–43.

  196. L.J. Selig, S. Clearwater, M. Lee and R. Engelsmore, Simulation and expert systems for finding particle beam line errors,Proc. 2nd AAAI Workshop on AI and Simulation (1987).

  197. G. Seliger, B. Viehweger, B. Wieneke-Toutouai and S.R. Kommana, Knowledge-based simulation of flexible manufacturing systems,Proc. 2nd European Simulation Multiconf. (1987) pp. 65–68.

  198. R.E. Shannon, Intelligent simulation environment,Proc. Intelligent Simulation Environments, Simulation Series 17:1 (SCSI, San Diego, CA, 1986) pp. 150–156.

    Google Scholar 

  199. R.E. Shannon, Knowledge based simulation techniques for manufacturing, Int. J. Prod. Res. 26(1988)953–973.

    Google Scholar 

  200. R.E. Shannon and M.A. Centeno, Expert simulation system based on a relational database,Proc. Winter Simulation Conf. (1990) pp. 412–414.

  201. R.E. Shannon, R. Mayer and H.H. Adelsberger, Expert systems and simulation, Simulation 44(1985)275–284.

    Google Scholar 

  202. G. Sheng and T.I. Ören, Computer-aided software understanding systems to enhance confidence of scientific codes,Proc. BIOMOVS (BIOspheric MOdel Validation Study) Symp. (1991) pp. 275–286.

  203. H.E. Shrobe, Understanding linkages,Papers from the AAAI Fall Symp. (1992) pp. 73–78.

  204. H.A. Simon, A. Newell, M.L. Minsky, G.A. Miller and S.S. Alexander, Simulation of human thinking, in:Computers and the World of the Future, ed. M. Greenberger (MIT Press, Cambridge, MA, 1962) pp. 94–131.

    Google Scholar 

  205. N. Singh, MARS: A multiple abstraction rule-based simulator, Memo HPP-83-43, Standford Heuristic Programming Project, Stanford University (1983).

  206. H.R. Smith and K. McVicar, Knowledge-based simulation with frameworks,Proc. Artificial Intelligence and Simulation: The Diversity of Applications (1988) pp. 72–77.

  207. L. Sokol, S. Geyer, R. Lasken and K. Murphy, ISLE — intelligent scalable logistics environment,Proc. Winter Simulation Conf. (1992) pp. 816–821.

  208. S. Soubra, R. Pelletret and W. Keilholz, Intelligent simulation environments: A first application to building construction.Proc. European Simulation Multiconf. (1993) pp. 222–226.

  209. M. Stelzner, J. Dynis and F. Cummins, The SIMKIT™ system: Knowledge-based simulation and modeling tools in KEE®,Proc. Winter Simulation Conf. (1989) pp. 232–234.

  210. Q.-Z. Sun, C.N. Chrystall and M.M. Kaye, Knowledge-based interactive real-time control system in discrete manufacturing,Proc. Artificial Intelligence and Simulation, Simulation Series 23:4 (SCSI, San Diego, CA, 1991) pp. 9–14.

    Google Scholar 

  211. Q.-Z. Sun, M.M. Kaye and C.N. Chrystall, The role of simulation in the development of an intelligent process control system, AI and simulation,Proc. AI and Simulation, Simulation Series 22:3 (SCSI, San Diego, CA, 1990) pp. 153–158.

    Google Scholar 

  212. J. Synder and G.T. Mackulack, Intelligent simulation environments: Identification of the basics,Proc. Winter Simulation Conf. (1988) pp. 357–363.

  213. E. Tanyi and D.A. Linkens, A frame-based modelling and simulation environment,Proc. UKSC Conf. on Computer Simulation (1987) pp. 1215–1219.

  214. W.M. Tao and X.R. Wang, ICAT: The design and implementation of intelligent control air combat target,Proc. 2nd Beijing Int. Conf. on System Simulation and Scientific Computing, (1992) pp. 299–303.

  215. R.P. Taylor and R.D. Hurrion, An expert advisor for simulation experimental design and analysis.Proc. Artificial Intelligence and Simulation: The Diversity of Applications (1988) pp. 238–244.

  216. S. Thangavadivelu and T.S. Colvin, Decision support system for crop production operations scheduling,Proc. AI and Simulation, Simulation Series 20:4 (SCSI, San Diego, CA, 1989) pp. 85–90.

    Google Scholar 

  217. J.U. Thoma, Bondgraphs for qualitative and quantitative systems modeling, in:Qualitative Simulation Modeling and Analysis, ed. P.A. Fishwick and P.A. Luker (Springer, New York, 1991) pp. 217–239.

    Google Scholar 

  218. S. Treu, Designing a “Cognizant Interface” between the user and the simulation software, Simulation 51(1988)227–234.

    Google Scholar 

  219. C. Tsatsoulis, A review of artificial intelligence in simulation, ACM SIGART Bull. 2(1991)115–121.

    Google Scholar 

  220. M. Turega, Neural networks, in:Concise Encyclopedia of Software Engineering, ed. D. Morris and B. Tamm (Pergamon Press, Oxford, UK, 1993) pp. 206–209.

    Google Scholar 

  221. M. Uschold et al., An intelligent front-end for ecological modelling, Proc. IJCAI (1985).

  222. W. van Braam, W. Dassen, P. Brugada and H. Wellens, Supportive interaction between rule-based and self-learning expert systems in biomedical research,Proc. Summer Computer Simulation Conf. (1985) pp. 465–469.

  223. H. Vangheluwe, L. Vermeersch and G.C. Vansteenkiste, Portable continuous simulation program generators, a tool for intelligent process control,Proc. European Simulation Symp. (1990) pp. 66–72.

  224. G.C. Vansteenkiste, E.H.M. Kerckhoffs and F. Broeckx, Intelligent process control and scheduling and discrete event systems.Proc. European Simulation Symp. (1990).

  225. G.C. Vansteenkiste and E.J.H. Kerckhoffs, DESiRE: Dynamic expert systems in real-time environments,Proc. 2nd Beijing Int. Conf. on System Simulation and Scientific Computing (1992) pp. 14–19.

  226. W. Wang and R. Bell, A knowledge-based system structure for the modelling of flexible manufacturing systems, Trans. Soc. Comp. Simul. 9(1992)159–173.

    Google Scholar 

  227. D. Waye, A. Terroux and M. Walters, SIMSMART: Dynamic simulation for automated control and optimization of complex industrial processes, in:Modelling and Simulation Methodology: Knowledge Systems' Paradigms, ed. M.S. Elzas, T.I. Ören and B.P. Zeigler (North-Holland, Amsterdam, 1989) pp. 177–188.

    Google Scholar 

  228. J. Weinroth and G. Madey, The interface between simulation and neurocomputing, Proc. AI and Simulation, Simulation Series 22:3 (SCSI, San Diego, CA, 1990) pp. 1–5.

    Google Scholar 

  229. R.N. Wendt, Application of program understanding and rule-based quality assurance to Slam II simulation programs, Master's Thesis, Ottawa-Carleton Institute for Computer Science, University of Ottawa, Ottawa, Ont., Canada (1993).

    Google Scholar 

  230. R.D. Williams (ed), Two approaches to machine intelligence, IEEE Comp. 25(1992)78–81.

  231. B.P. Wise, R.B. Modjeski, Uncertainty management in battle-planning software, in:Knowledge-Based Simulation: Methodology and Application, ed. P.A. Fishwick and R.B. Modjeski (Springer, New York, 1991) pp. 261–276.

    Google Scholar 

  232. R.M. Wnek and S. Springsteen, Application of discrete event simulation techniques to the modeling of asynchronous neural network updating,Proc. AI and Simulation, Simulation Series 22:3 (SCSI, San Diego, CA, 1990) pp. 53–58.

    Google Scholar 

  233. S.-Y. Wu and R.A. Wysk, MPECS — An intelligent flexible machining cell controller,Proc. European Simulation Multiconf. (1987) pp. 71–76.

  234. G. Xiong and A. Song, An expert system for dynamic system simulation,Proc. AI Applied to Simulation, Simulation Series 18:1 (SCSI, San Diego, 1986) pp. 106–110.

    Google Scholar 

  235. L. Yizhi and J.W. Wen, The research of knowledge-based simulation environment KBSE-1,Proc. 2nd Beijing Int. Conf. on System Simulation and Scientific Computing (1992) pp. 105–109.

  236. V.V. Yukhimov and A.I. Gamarnik, Simulation models self-organization at the systems parameters estimation,Proc. 1993 European Simulation Multiconf. (1993) pp. 371–375.

  237. W.A. Zajicek, Transforming a discrete-event system into a logic programming formalism, in:Modelling and Simulation Methodology in the Artificial Intelligence Era, ed. M.S. Elzas, T.I. Ören, B.P. Zeigler (North-Holland, Amsterdam, 1986) pp. 181–192.

    Google Scholar 

  238. P.A. Zalevsky, Knowledge-based simulation of manufacturing facilities,Proc. Artificial Intelligence and Simulation: The Diversity of Applications (SCSI, San Diego, CA, 1988) pp. 67–71.

    Google Scholar 

  239. B.P. Zeigler, DEVS-scheme: A Lisp-based environment for hierarchical, modular discrete event models, Technical Report AIS-2, Computer Engineering Lab., Department. of Electrical and Computer Engineering, University. of Arizona, Tucson, AZ (1986).

    Google Scholar 

  240. B.P. Zeigler,Object-Oriented Simulation with Hierarchical, Modular Models — Intelligent Agents and Endomorphic Systems (Academic Press, Boston, MA, 1990).

    Google Scholar 

  241. B.P. Zeigler, C.-J. Luhand T.-G. Kim, Model base management for multifacetted systems, in:AI, Simulation and Planning in High Autonomy Systems, ed. B.P. Zeigler and J.W. Rozenblit (IEEE Computer Society Press, Los Alomitos, CA, 1990) pp. 25–31.

    Google Scholar 

  242. B.P. Zeigler, J.W. Rozenblit and E.R. Christensen, Reducing the validation bottleneck with a knowledge-based distributed simulation environment, Expert Syst. Appl. 3(1991)329–342.

    Google Scholar 

  243. G.-t. Zhang, An expert simulation system (SAD-1) for effectiveness/cost analysis of a surface-to-air defence system,Proc. 2nd Beijing Int. Conf. on System Simulation and Scientific Computing (1992) pp. 498–502.

  244. J.-G. Zhao and B.-L. Li, An expert mathematical modelling system — EMMS,Proc. Beijing Int. Conf. on System Simulation and Scientific Computing, vol. 1 (1989) pp. 520–524.

    Google Scholar 

  245. J.-G. Zhao and B.-L. Li, An expert mathematical modelling system — EMMS,Proc. Summer Computer Simulation Conf. (1991) pp. 1066–1071.

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Ören, T.I. Artificial intelligence in simulation. Ann Oper Res 53, 287–319 (1994). https://doi.org/10.1007/BF02136832

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