Abstract
Virtual reality (VR) is an evolving area for simulation and training applications. The area is related to realistic and interactive experiments in real time computational systems. Those systems generate data and information that can be used to intelligent decision making, as skills evaluation related to dexterity and reasoning power in critical procedures.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Bibliography
Alca˜niz, M. et al. (2003), GeRTiSS: Generic Real Time Surgery Simulation, Studies in Health Technology and Informatics, 94, pp. 16–18, IOSPress.
Baum, L.E. (1972), An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes, Inequalities, (3), pp. 1–8.
Baum, L.E. and Petrie, T. (1996), Statistical inference for probabilistic functions of finite state Markov chains, Ann. Math. Stat., (37), pp. 1554–1563.
Bezdek, J.C. (1993), A review of probabilistic, fuzzy and neural models for pattern recognition, Journal of Intelligent and Fuzzy Systems, 1 (1), pp. 1–26.
Bonnet, N. and Cutrona, J. (2001), Improvement of unsupervised multi-component image segmentation through fuzzy relaxation, Proc of IASTED Int. Conf. on Visualization, Imaging and Image Processing, pp. 477–482, Spain.
Borgelt, C. and Kruse, R. (2002), Graphical Models: Methods for Data Analysis and Mining, Wiley.
Borotschnig, H, et al. (1998), Fuzzy relaxation labeling reconsidered, Proc. IEEE World Congress On Computational Intelligence, FUZZ-IEEE, pp. 1417–1423.
Buchanan, B.G. and Shortlife, E.H. (1985), Rule-Based Expert Systems: The MYCIN experiments of the Stanford Heuristic Programming Project, Addison-Wesley.
Cheng, J. and Greiner, R. (2001), Learning Bayesian Belief Network Classifiers: Algorithms and System, Proc. Fourteenth Canadian Conference on Artificial Intelligence.
Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977), Maximum Likelihood from Incomplete Data via EM Algorithm, Journal of Royal Statistical Society, Ser. B. 39, pp. 1–38.
Doring, C., Borgelt, C. and Kruse, R. (2004), Fuzzy clustering of quantitative and qualitative data, Proc. of the 2004 NAFIPS, pp. 84–89.
Dubois, D. and Prade, H. (1996), What are fuzzy rules and how to use them. Fuzzy Sets and Systems, 84 (1), pp. 169–185.
Fu, K.S. and Yu, T.S. (1980), Statistical Pattern Classification Using Contextual Information, Research Studies Press.
Gande, A. and Devarajan, V. (2003), Instructor station for virtual laparoscopic surgery: requirements and design, Proc. of Computer Graphics and Imaging, USA, pp. 85–90.
Gao, Z. and L´ecuyer, A. (2008), A VR Simulator for Training and Prototyping of Telemanipulation of Nanotubes, Proc. ACM Simp. on Virtual Reality Software and Technology, Bordeaux/France, pp. 101–104.
Harstela, P. (1999), The Future of Timber Harvesting in Finland, Int. Journal of Forest Engineering, 10 (2), pp. 33–36.
Hummel, R.A. and Zucker, S.W. (1983), On the foundations of relaxation labeling processes, IEEE Transactions on Pattern Analysis and Machine Intelligence, 5 (3), pp. 267–287.
John, N.W. et al. (2001), Web-based surgical educational tools, Studies in Health Technology and Informatics, 81, pp. 212–217. IOSPress.
Johnson, R.A. andWichern, D.W. (2001), Applied Multivariate Statistical Analysis, 5th edition, Prentice Hall.
Juang, B.-H. and Rabiner, L.R. (1990), The segmental K-means algorithm for estimating parameters of hidden Markov models, IEEE Trans. Acoustics, Speech and Signal Processing, 38 (9), pp. 1639–1641.
Krause, P.J. (1998), Learning Probabilistic Networks, Knowledge Engineering Review, 13, pp. 321–351.
Machado, L.S. and Moraes, R.M. (2003), An Online Evaluation Of Training in Virtual Reality Simulators Using Fuzzy GaussianMixtureModels and Fuzzy Relaxation Labeling, Proc. IASTED International Conference on Computers and Advanced Technology in Education (CATE’2003), pp. 192–196, Greece.
Machado, L.S. and Moraes, R.M. (2006), VR-Based Simulation for the Learning of Gynaecological Examination. Lecture Notes in Computer Science, 4282, pp. 97–104.
Machado, L.S., Moraes, R.M. and Zuffo, M.K. (2000), A Fuzzy Rule-Based Evaluation for a Haptic and Stereo Simulator for Bone Marrow Harvest for Transplant, Proc. Phantom Users Group Workshop, USA.
Machado, L.S., Mello, A.N., Lopes, R.D., Odone Fo., V. and Zuffo, M.K. (2001), A Virtual Reality Simulator for BoneMarrow Harvest for Transplant, Studies in Health Technology and Informatics, Amsterdam/The Netherlands, 81, pp. 293 297.
Machado, L.S. et al. (2006), Assessement of Gynecological Procedures in a Simulator Based on Virtual Reality, Proc. 7th International FLINS Conference on Applied Artificial Intelligence, pp. 799–804, Italy.
Machado, L.S. and Moraes, R.M. (2009), Qualitative and Quantitative Assessment for a VRBased Simulator, Studies in Health Technology and Informatics, 142, pp. 168–173, IOS Press.
Mamdani, E.H. and Assilian, S. (1975), An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller, International Journal of Man-Machine Studies, 7, pp. 1–13.
McLachlan, G. and Peel, D. (2000), Finite Mixture Models, New York: Wiley-Interscience.
Mohamed, M.A. and Gader, P. (2000), Generalized hidden Markov models - Part I: theoretical frameworks, IEEE Trans. On Fuzzy Systems, 8 (1), pp. 67–81.
Mood, A.M., Graybill, F. and Boes, D.C. (1974), Introduction to the Theory of Statistics, 3rd ed., McGraw-Hill.
Moraes, R.M. and Machado, L.S. (2003), Online Training Evaluation in Virtual Reality Simulators Using Gaussian Mixture Models, Studies in Health Technology and Informatics, 94, pp. 42–44, IOSPress.
Moraes, R.M. and Machado, L.S. (2003), Fuzzy Gaussian Mixture Models for On-line Training Evaluation in Virtual Reality Simulators. Annals of the International Conference on Fuzzy Information Processing (FIP’2003), 2, pp. 733–740, China.
Moraes, R.M. and Machado, L.S. (2003), Gaussian Mixture Models and Relaxation Labeling for Online Evaluation of Training in Virtual Reality Simulators, Proc. Int. Conf. on Engineering and Computer Education (ICECE’2003), Brasil.
Moraes, R.M. and Machado, L.S. (2004), Using Fuzzy Hidden Markov Models for Online Training Evaluation and Classification in Virtual Reality Simulators, International Journal of General Systems, 33 (2-3), pp. 281–288.
Moraes, R.M. and Machado, L.S. (2005), Maximum Likelihood for On-line Evaluation of Training Based on Virtual Reality, Proc. Global Congress on Engineering and Technology Education (GCETE’2005), pp. 299–302, Brazil.
Moraes, R.M. and Machado, L.S. (2005), Continuous Evaluation in Training Systems Based on Virtual Reality, Proc. Global Congress on Engineering and Technology Education (GCETE’2005), pp. 1048–1051, Brazil.
Moraes, R.M. and Machado, L.S. (2006), On-line Training Evaluation in Virtual Reality Simulators using Fuzzy Bayes Rule, Proc. 7th International FLINS Conference on Applied Artificial Intelligence (FLINS’2006), pp. 791–798, Italy.
Moraes, R.M. and Machado, L.S. (2007), Assessment Based on Naive Bayes for Training Based on Virtual Reality, Proc. Int. Conference on Engineering and Computer Education (ICECE’2007), pp. 269–273, Brazil.
Moraes, R.M. and Machado, L.S. (2007), Multiple Assessment for Multiple Users in Virtual Reality Training Environments, Lecture Notes in Computer Science, 4756, pp. 950–956, Berlin.
Moraes, R.M. and Machado, L.S. (2008), A Modified Naive Bayes to Online Training Assessment in Virtual Reality Simulators, Proc. 3th Int. Conf. on Intelligent System and Knowledge Engineering (ISKE’2008), China.
Moraes, R.M. and Machado, L S. (2008), Using Embedded Systems to Improve Performance of Assessment in Virtual Reality Training Environments, Proc. Int. Conference on Engineering and Technology Education (Intertech’2008), pp. 140–144, Brazil.
Moraes, R.M.,Machado, L.S. and Souza, L.C. (2009), Online Assessment of Training in Virtual Reality Simulators Based on General Bayesian Networks, Proc. VI International Conference on Engineering and Computer Education (ICECE’2009), Argentina, In Press.
Neapolitan, R.E. (2003), Learning Bayesian Networks, Prentice Hall Series in Artificial Intelligence, Prentice Hall.
Peleg, S. and Rosenfeld, A. (1978), Determining Compatibility Coefficients for Curve Enhancement
Relaxation-Process, IEEE Trans. on Systems, Man and Cybernetics, 8 (7), pp. 548–555.
Rabiner, L.R. (1989), A Tutorial on HiddenMarkov Models and Selected Application in Speech Recognition, Proc. of the IEEE, 77 (2).
Rabiner, L.R and Juang, B.-H. (1993), Fundamentals of Speech Recognition, Prentice Hall PTR, New Jersey.
Rheingold, H. (1991), Virtual Reality, Touchstone, New York.
Rich, E. and Knight, K. (1993), Artificial Intelligence. New York: McGrawHill.
Rose, F., Brooks, B. and Rizzo, A. (2005), Virtual Reality in Brain Damage Rehabilitation: Review, CyberPsychology and Behavior, 8 (3), pp. 241–262.
Rosenfeld, A., Hummel, R.A. and Zucker, S.W. (1976), Scene labelling by relaxation operations, IEEE Transactions on Systems, Man and Cybernetics, 6 (6), pp. 420–433.
Ryan, M.S. and Nudd, G.R. (1993), The Viterbi algorithm, Research Report CS-RR-238, Department of Computer Science, University of Warwick, UK.
Shafer, G. (1976), A Mathematical Theory of Evidence, Princeton University Press.
Terano, T., Asai, K. and Sugeno, M. (1987), Fuzzy systems theory and it’s applications, Academic Press Inc., San Diego.
Tran, D. and Wagner, M. (1998), Fuzzy Gaussian Mixture Models for Speaker Recognition, Proc. Int. Conf. Spoken Language Processing (ICSLP98) special issue of the Australian Journal of Intelligent Information Processing Systems (AJIIPS), 5 (4), pp. 293–300.
Tran, D. and Wagner, M. (1999), Fuzzy approach to Gaussian mixture models and generalised Training Evaluation and Classification in Virtual Reality Simulators, International Journal of General Systems, 33 (2-3), pp. 281–288.
Moraes, R.M. and Machado, L.S. (2005), Maximum Likelihood for On-line Evaluation of Training Based on Virtual Reality, Proc. Global Congress on Engineering and Technology Education (GCETE’2005), pp. 299–302, Brazil.
Moraes, R.M. and Machado, L.S. (2005), Continuous Evaluation in Training Systems Based on Virtual Reality, Proc. Global Congress on Engineering and Technology Education (GCETE’2005), pp. 1048–1051, Brazil.
Moraes, R.M. and Machado, L.S. (2006), On-line Training Evaluation in Virtual Reality Simulators
using Fuzzy Bayes Rule, Proc. 7th International FLINS Conference on Applied Artificial Intelligence (FLINS’2006), pp. 791–798, Italy.
Moraes, R.M. and Machado, L.S. (2007), Assessment Based on Naive Bayes for Training Based on Virtual Reality, Proc. Int. Conference on Engineering and Computer Education (ICECE’2007), pp. 269–273, Brazil.
Moraes, R.M. and Machado, L.S. (2007), Multiple Assessment for Multiple Users in Virtual Reality Training Environments, Lecture Notes in Computer Science, 4756, pp. 950–956, Berlin.
Moraes, R.M. and Machado, L.S. (2008), A Modified Naive Bayes to Online Training Assessment in Virtual Reality Simulators, Proc. 3th Int. Conf. on Intelligent System and Knowledge Engineering (ISKE’2008), China.
Moraes, R.M. and Machado, L S. (2008), Using Embedded Systems to Improve Performance of Assessment in Virtual Reality Training Environments, Proc. Int. Conference on Engineering and Technology Education (Intertech’2008), pp. 140–144, Brazil.
Moraes, R.M.,Machado, L.S. and Souza, L.C. (2009), Online Assessment of Training in Virtual Reality Simulators Based on General Bayesian Networks, Proc. VI International Conference on Engineering and Computer Education (ICECE’2009), Argentina, In Press.
Neapolitan, R.E. (2003), Learning Bayesian Networks, Prentice Hall Series in Artificial Intelligence, Prentice Hall.
Peleg, S. and Rosenfeld, A. (1978), Determining Compatibility Coefficients for Curve Enhancement
Relaxation-Process, IEEE Trans. on Systems, Man and Cybernetics, 8 (7), pp. 548–555.
Rabiner, L.R. (1989), A Tutorial on HiddenMarkov Models and Selected Application in Speech Recognition, Proc. of the IEEE, 77 (2).
Rabiner, L.R and Juang, B.-H. (1993), Fundamentals of Speech Recognition, Prentice Hall PTR, New Jersey.
Rheingold, H. (1991), Virtual Reality, Touchstone, New York.
Rich, E. and Knight, K. (1993), Artificial Intelligence. New York: McGrawHill.
Rose, F., Brooks, B. and Rizzo, A. (2005), Virtual Reality in Brain Damage Rehabilitation: Review, CyberPsychology and Behavior, 8 (3), pp. 241–262.
Rosenfeld, A., Hummel, R.A. and Zucker, S.W. (1976), Scene labelling by relaxation operations, IEEE Transactions on Systems, Man and Cybernetics, 6 (6), pp. 420–433.
Ryan, M.S. and Nudd, G.R. (1993), The Viterbi algorithm, Research Report CS-RR-238, Department of Computer Science, University of Warwick, UK.
Shafer, G. (1976), A Mathematical Theory of Evidence, Princeton University Press.
Terano, T., Asai, K. and Sugeno, M. (1987), Fuzzy systems theory and it’s applications, Academic Press Inc., San Diego.
Tran, D. and Wagner, M. (1998), Fuzzy Gaussian Mixture Models for Speaker Recognition, Proc. Int. Conf. Spoken Language Processing (ICSLP98) special issue of the Australian Journal of Intelligent Information Processing Systems (AJIIPS), 5 (4), pp. 293–300.
Tran, D. and Wagner, M. (1999), Fuzzy approach to Gaussian mixture models and generalised Gaussian mixture models, Proc. Computation Intelligence Methods and Applications, (CIMA’99), 154–158, USA.
Tran, D. and Wagner, M. (1999), Fuzzy hidden Markov models for speech and speaker recognition, Proc. 18th Int. Conf. North American Fuzzy Information Society (NAFIPS’99), pp. 426–430, USA.
Tran, D., VanLe, T. and Wagner, M. (1998), Fuzzy Gaussian Mixture Models for Speaker Recognition, Proc. Int. Conf. on Spoken Language Processing (ICSLP98), 2, pp. 759–762, Australia.
Tran, D., Pham, T. and Wagner, M. (1999), Speaker recognition using Gaussian mixture models and relaxation labeling, Proc. 3rd World Multiconf. on Systemetics, Cybernetics and Informatics/ 5th Int. Conf. Information Systems Analysis and Synthesis (SCI/ISAS99), 6, pp. 383–389.
Tran, D., Wagner, M. and Zheng, T. (1999), A fuzzy approach to statistical models in speech and speaker recognition, Proc. FUZZ-IEEE’99 Conference 3, pp. 1275–1280, Korea.
Weiss, P., Rand, D., Katz, N. and Kizony, R. (2004), Video capture virtual reality as a flexible and effective rehabilitation tool. Journal of NeuroEngineering and Rehabilitation, 1 (12), online (http://www.jneuroengrehab.com/content/1/1/12), BioMed Central.
Yang, Y and Webb, F.I. (2003), On Why Discretization Works for Naive-Bayes Classifiers, Lecture Notes on Artificial Intelligence, 2903, pp. 440–452.
Zadeh, L.A. (1965), Fuzzy Sets, Information and Control, 8, pp. 338–353.
Zadeh, L.A. (1968), Probability Measures of Fuzzy Events, Journal of Mathematical Analisys and Applications, 10, pp. 421–427.
Zadeh, L.A. (1988), Fuzzy Logic, Computer, 1, pp. 83–93.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2010 Atlantis Press/World Scientific
About this chapter
Cite this chapter
dos Santos Machado, L., de Moraes, R.M. (2010). Intelligent DecisionMaking in Training Based on Virtual Reality. In: Computational Intelligence in Complex Decision Systems. Atlantis Computational Intelligence Systems, vol 2. Atlantis Press. https://doi.org/10.2991/978-94-91216-29-9_4
Download citation
DOI: https://doi.org/10.2991/978-94-91216-29-9_4
Publisher Name: Atlantis Press
Online ISBN: 978-94-91216-29-9
eBook Packages: Computer ScienceComputer Science (R0)