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During the simulation, the simulation environment continuously changes and simulation objects correspondingly behave according to the changing situations. In general, modeling the behavior for all possible situations is extremely difficult when the rationale is unknown. Therefore, in order to adapt to the changing situation, it is important to recognize the rationale behind the behaviors of the simulation object. However, in many cases, even though the rationale is unknown or difficult to recognize, the simulation requires reasonable behaviors such as a commander\u2019s decision in a war game simulation and a driver\u2019s behavior in rush hours. In this study, we propose a new approach to determine the behavior of simulation objects under changing situations. The proposal is a unified learning approach that integrates two methods, data-driven and knowledge-driven approaches, which allow simulation objects to learn behavioral knowledge from experience as well as from domain experts performing the simulation and reuse verified knowledge. By combining both approaches, we supplement the shortcomings of one method with the strengths of the other. To verify our method, we apply the proposed approach to a military training simulation.<\/jats:p>","DOI":"10.1177\/0037549717753880","type":"journal-article","created":{"date-parts":[[2018,2,2]],"date-time":"2018-02-02T09:48:43Z","timestamp":1517564923000},"page":"979-992","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Unified learning to enhance adaptive behavior of simulation objects"],"prefix":"10.1177","volume":"94","author":[{"given":"Hyo-Cheol","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Ajou University, Suwon-si, Gyeonggi-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seok-Won","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Software and Computer Engineering, Ajou University, Suwon-si, Gyeonggi-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2018,2,2]]},"reference":[{"key":"bibr1-0037549717753880","first-page":"9","volume-title":"Proceedings of the 36th conference on winter simulation","author":"Carson II"},{"key":"bibr2-0037549717753880","doi-asserted-by":"publisher","DOI":"10.2507\/IJSIMM12(4)4.249"},{"key":"bibr3-0037549717753880","first-page":"689","volume-title":"Proceedings of 2014 Korea computer congress","author":"Lee HC"},{"issue":"2","key":"bibr4-0037549717753880","first-page":"49","volume":"11","author":"Lee HC","year":"2015","journal-title":"J KING Comput"},{"key":"bibr5-0037549717753880","first-page":"137","volume-title":"Machine learning","author":"Carbonell JG.","year":"1983"},{"key":"bibr6-0037549717753880","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2010.2071862"},{"key":"bibr7-0037549717753880","first-page":"64","volume-title":"AAAI Spring Symposium: To Boldly Go Where No Human-Robot Team Has Gone Before","author":"Koenig NP"},{"key":"bibr8-0037549717753880","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(90)90040-7"},{"key":"bibr9-0037549717753880","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-007-0046-2"},{"key":"bibr10-0037549717753880","author":"Von Wangenheim CG","year":"2000","journal-title":"Universidade do Vale do Itajai"},{"key":"bibr11-0037549717753880","doi-asserted-by":"publisher","DOI":"10.3233\/AIC-1994-7104"},{"key":"bibr12-0037549717753880","first-page":"931","volume-title":"International ICSC\/IFAC Symposium on Neural Computation","author":"Whar SY"},{"key":"bibr13-0037549717753880","first-page":"47","volume-title":"International Conference on Machine Learning","author":"Benson S"},{"key":"bibr14-0037549717753880","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2012.04.013"},{"key":"bibr15-0037549717753880","volume-title":"Proc. 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