Abstract
Military decision support and simulation training tools are mostly complex and large-scale IT systems and therefore multi-resolution distributed simulation models have been playing a leading role. The paper considers an approach which combines a graph theory, HLA simulation standard, a special ontology and rough set formalisms into a synergistic software. The first issue is the way to enhance HLA object model by an ontology. The subsequent problem is construction of a software plugin to explicit handle shared information. Furthermore, the Rough Set Theory provides the solid foundation for the construction of classifiers as well as generation of decision rules from dataset. The proposed approach might be perceived in terms of distributed computational intelligence and ontology-based information sharing.
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Pierzchała, D., Dyk, M., Szydłowski, A.: Distributed Military Simulation Augmented by Computational Collective Intelligence. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part I. LNCS, vol. 6922, pp. 399–408. Springer, Heidelberg (2011)
Pierzchała, D.: Designing and testing method of distributed interactive simulators. In: 15th International Conference on Systems Science, Wroclaw (2004) ISBN 83-7085-805-8
Pierzchala, D., Szymański, P.: Ontology-based adapter for data of multi-resolution battlefield simulation. In: XVIII Workshop of the Society For Computer Simulation, Poland (2011)
Pierzchała, D., Najgebauer, A., Antkiewicz, R., Chmielewski, M., Rulka, J., Wantoch-Rekowski, R., Tarapata, Z., Drozdowski, T.: Knowledge-Based Approach for Military Mission Planning and Simulation. In: Gutiérrez, C.R. (ed.) Advances in Knowledge Representation, pp. 251–272. InTech (2012) ISBN 978-953-51-0597-8
Natrajan, A., Reynolds, P.F., Srinivasan, S.: Guidelines for the Design of Multiresolution Simulations. US DoD DMSO (July 1997)
Su-Youn, H., Tag Gon, K.: Specification of multi-resolution modeling space for multiresolution system simulation. Transactions of the Society for Modeling and Simulation International 89(1), 28–40 (2012), doi:10.1177/0037549712450361
ShangGuan, W., Bai-gen, C., Si-Hui, L., Zhen-Guo, L., Jian, W.: Multi-resolution simulation strategy and its simulation implementation of Train Control System. IEEE (2011) 978-1-4577-0574-8/11
Cayirci, E.: Multi-Resolution Federations In Support Of Operational And Higher Level Combined/Joint Computer Assisted Exercises. In: IEEE Proceedings of the 2009 Winter Simulation Conference (2009) 978-1-4244-5771-7/09
Turnitsa, T., Tolk, A.: Federated Ontologies Supporting a Merged Worldview for Distributed Systems. In: Association for Advancements in Artificial Intelligence (AAAI) Fall Symposium, Technical Report FS-07-06, pp. 116–119. AAAI Press, Menlo Park (2007)
Hu, J., Zhang, H.: Ontology Based Collaborative Simulation Framework Using HLA & Web Services. IEEE, 116–119 (2008) 978-0-7695-3507-4/08
Jia, M., Yang, B., Zheng, D., Sun, W.: Research on Domain Ontology Construction in Military Intelligence. IEEE (2009), doi:10.1109/IITA.2009.80, 978-0-7695-3859-4/09
Silver, G., Hassan, O., Miller, J.: From Domain Ontologies To Modeling Ontologies To Executable Simulation Models. In: Proceedings of Winter Simulation Conference (2007)
Bowman, M., Lopez, A., Tecuci, G.: Ontology Development for Military Applications. Report from DARPA grants: no. F49620−97−1−0188 and no. F49620−00−1−0072 (2007)
Bazan, J., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough set algorithms in classification problem. In: Polkowski, Tsumoto, Lin (eds.) Rough Set Methods and Applications. Physica-Verlag, Heidelberg (2000) ISBN:3-7908-1328-1
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Rissino, S., Lambert-Torres, G.: Rough Set Theory – Fundamental Concepts, Principals, Data Extraction, and Applications. In: Ponce, Karahoca (eds.) Data Mining and Knowledge Discovery in Real Life Applications. InTech (2009) ISBN 978-3-902613-53-0
Skowron, A., Nguyen, H.: Rough Sets: From Rudiments to Challenges. In: Skowron, Suraj (eds.) Rough Sets And Intelligent Systems (2013) ISBN 978-3-642-30344-9
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Słowiński, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, pp. 311–362. Kluwer Academic Publishers, Dordrecht (1992)
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Pierzchała, D. (2014). Application of Ontology and Rough Set Theory to Information Sharing in Multi-resolution Combat M&S. In: Sobecki, J., Boonjing, V., Chittayasothorn, S. (eds) Advanced Approaches to Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 551. Springer, Cham. https://doi.org/10.1007/978-3-319-05503-9_19
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DOI: https://doi.org/10.1007/978-3-319-05503-9_19
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