Abstract
Providing assistance systems for simulation studies can support the user by performing monotonous tasks and keeping track of relevant results. In this paper we present approaches to estimate, if – and when – statistically significant results are expected for certain investigations. This information can be used to control simulation runs or to provide information to the user for interaction. The first approach is used to classify if significance is expected to occur for given samples and the second approach estimates the needed replications until significance is expected be reached. For an initial evaluation of the approaches, experiments are performed on samples drawn from normal distributions.
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Bianchi, L., Dorigo, M., Gambardella, L.M., Gutjahr, W.J.: A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing: An International Journal 8(2), 239–287 (2009)
Burl, M.C., DeCoste, D., Enke, B.L., Mazzoni, D., Merline, W.J., Scharenbroich, L.: Automated knowledge discovery from simulators. In: Ghosh, J., Lambert, D., Skillicorn, D.B., Srivastava, J. (eds.) Proceedings of the Sixth SIAM International Conference on Data Mining, Bethesda, MD, USA, April 20-22 (2006)
Ekren, B.Y., Heragu, S.S.: Simulation based optimization of multi-location transshipment problem with capacitated transportation. In: WSC 2008: Proceedings of the 40th Conference on Winter Simulation, pp. 2632–2638 (2008)
Hoad, K., Robinson, S., Davies, R.: Automated selection of the number of replications for a discrete-event simulation. Journal of the Operational Research Society (October 2009), http://dx.doi.org/10.1057/jors.2009.121
Huber, K.P., Syrjakow, M., Szczerbicka, H.: Extracting knowledge supports model optimization. In: Proceedings of the International Simulation Technology Conference, SIMTEC 1993, San Francisco, pp. 237–242 (November 1993)
King, R.D., Rowland, J., Oliver, S.G., Young, M., Aubrey, W., Byrne, E., Liakata, M., Markham, M., Pir, P., Soldatova, L.N., Sparkes, A., Whelan, K.E., Clare, A.: The automation of science. Science 324(5923), 85–89 (2009)
King, R.D., Whelan, K.E., Jones, F.M., Reiser, P.G.K., Bryant, C.H., Muggleton, S.H., Kell, D.B., Oliver, S.G.: Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247–252 (2004)
Klösgen, W.: Exploration of simulation experiments by discovery. In: AAAI 1994 Workshop on Knowledge Discovery in Databases (KDD 1994), Technical Report WS-94-03. pp. 251–262. The AAAI Press, Menlo Park (1994)
Klösgen, W.: Explora: A multipattern and multistrategy discovery assistant. In: Fayyad, U.M., Piatetsky-Shapiro, G., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 249–271. AAAI Press, Menlo Park (1996)
Laganá, D., Legato, P., Pisacane, O., Vocaturo, F.: Solving simulation optimization problems on grid computing systems. Parallel Comput. 32(9), 688–700 (2006)
Law, A.M.: Simulation Modeling & Analysis, 4th edn. McGraw-Hill (2007)
Park, H.M.: Hypothesis testing and statistical power of a test. Working paper. The university information technology services (UITS), Center for Statistical and Mathematical Computing, Indiana University (2008)
Quinlan, J.R.: C4.5 - Programs for Machine Learning. Morgan Kaufmann Publishers, Inc. (1993)
R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2010), http://www.R-project.org ; ISBN 3-900051-07-0
Swisher, J.R., Jacobson, S.H.: Evaluating the design of a family practice healthcare clinic using discrete-event simulation. Health Care Management Science 5(2), 75–88 (2002)
Swisher, J.R., Jacobson, S.H., Yücesan, E.: Discrete-event simulation optimization using ranking, selection, and multiple comparison procedures: A survey. ACM Trans. Model. Comput. Simul. 13(2), 134–154 (2003)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
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Lattner, A.D., Bogon, T., Timm, I.J. (2013). Convergence Classification and Replication Prediction for Simulation Studies. In: Filipe, J., Fred, A. (eds) Agents and Artificial Intelligence. ICAART 2011. Communications in Computer and Information Science, vol 271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29966-7_17
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DOI: https://doi.org/10.1007/978-3-642-29966-7_17
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