{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:49:33Z","timestamp":1777704573689,"version":"3.51.4"},"reference-count":28,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,4,22]]},"abstract":"<jats:p>It is a challenge for existing artificial intelligence algorithms to deal with incomplete information of computer tactical wargames in military research, and one effective method is to take advantage of game replays based on data mining or supervised learning. However, the open source datasets of wargame replays are extremely rare, which obstruct the development of research on computer wargames. In this paper, a data set of wargame replays is opened for predicting algorithm on the condition of incomplete information, to be specific, we propose the dataset processing method for deep learning and an network model for enemy locations predicting. We first introduce the criteria and methods of data preprocessing, parsing and feature extraction, then the training set and test set for deep learning are predefined. Furthermore, we have designed a newly specific network model for enemy locations predicting, including multi-head input, multi-head output, CNN and GRU layers to deal with the multi-agent and long-term memory problems. The experimental results demonstrate that our method achieves good performance of 84.9% on top-50 accuracy. Finally, we open source the data set and methods on https:\/\/github.com\/daman043\/AAGWS-Wargame-master.<\/jats:p>","DOI":"10.3233\/jifs-201726","type":"journal-article","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T18:17:30Z","timestamp":1612894650000},"page":"9259-9275","source":"Crossref","is-referenced-by-count":7,"title":["Introduction of a new dataset and method for location predicting based on deep learning in wargame"],"prefix":"10.1177","volume":"40","author":[{"given":"Man","family":"Liu","sequence":"first","affiliation":[{"name":"Army Engineering University of PLA, Nanjin, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Army Engineering University of PLA, Nanjin, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenning","family":"Hao","sequence":"additional","affiliation":[{"name":"Army Engineering University of PLA, Nanjin, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuli","family":"Qi","sequence":"additional","affiliation":[{"name":"Army Engineering University of PLA, Nanjin, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Cheng","sequence":"additional","affiliation":[{"name":"Army Engineering University of PLA, Nanjin, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dawei","family":"Jin","sequence":"additional","affiliation":[{"name":"Army Engineering University of PLA, Nanjin, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinliang","family":"Feng","sequence":"additional","affiliation":[{"name":"Army Infantry Academy of PLA, Nanchang, Jiangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"7540","key":"10.3233\/JIFS-201726_ref1","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"issue":"7587","key":"10.3233\/JIFS-201726_ref2","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"issue":"7553","key":"10.3233\/JIFS-201726_ref3","first-page":"436","article-title":"Deepmind lab","volume":"521","author":"Beattie","year":"2015","journal-title":"arXiv preprint"},{"issue":"7553","key":"10.3233\/JIFS-201726_ref4","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"1","key":"10.3233\/JIFS-201726_ref5","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TETCI.2018.2823329","article-title":"Starcraft micromanagement with reinforcement learning and curriculum transfer learning","volume":"3","author":"Shao","year":"2018","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"issue":"7782","key":"10.3233\/JIFS-201726_ref7","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1038\/s41586-019-1724-z","article-title":"Grandmaster level in starcraft ii using multi-agent reinforcement learning","volume":"575","author":"Vinyals","year":"2019","journal-title":"Nature"},{"key":"10.3233\/JIFS-201726_ref8","unstructured":"Dunnigan J.F. , The complete wargames handbook: How to play, design, and find them, William Morrow & Co., Inc., (1992)."},{"key":"10.3233\/JIFS-201726_ref11","unstructured":"Perla P.P. , The art of wargaming: A guide for professionals and hobbyists, Naval Institute Press, (1990)."},{"key":"10.3233\/JIFS-201726_ref12","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.actaastro.2017.10.016","article-title":"The politics of space mining \u2013 an account of a simulation game","volume":"142","author":"Paikowsky","year":"2018","journal-title":"Acta Astronautica"},{"issue":"3","key":"10.3233\/JIFS-201726_ref13","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s10994-006-8919-x","article-title":"Machine learning and games","volume":"63","author":"Bowling","year":"2006","journal-title":"Machine Learning"},{"issue":"2","key":"10.3233\/JIFS-201726_ref15","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1177\/1046878102332002","article-title":"The uses of teaching games in game theory classes and some experimental games","volume":"33","author":"Shubik","year":"2002","journal-title":"Simulation & Gaming"},{"key":"10.3233\/JIFS-201726_ref18","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.futures.2018.10.001","article-title":"Combining scenario planning and business wargaming to better anticipate future competitive dynamics","volume":"105","author":"Schwarz","year":"2019","journal-title":"Futures"},{"key":"10.3233\/JIFS-201726_ref20","doi-asserted-by":"crossref","unstructured":"Dong M. , Mei X. , Qi X. , Hou L. and Li J. , Research on the advantages and equilibrium of computer game with incomplete information, in 2017 29th Chinese Control And 17 Decision Conference (CCDC). IEEE, (2017), 7675\u20137678.","DOI":"10.1109\/CCDC.2017.7978581"},{"issue":"6337","key":"10.3233\/JIFS-201726_ref21","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1126\/science.aam6960","article-title":"Deepstack: Expert-level artificial intelligence in heads-up nolimit poker","volume":"356","author":"Morav\u010d\u00edk","year":"2017","journal-title":"Science"},{"key":"10.3233\/JIFS-201726_ref25","doi-asserted-by":"crossref","unstructured":"Moy G. and Shekh S. , The application of alphazero to wargaming, in AI 2019: Advances in Artificial Intelligence. Springer International Publishing (2019), 3\u201314.","DOI":"10.1007\/978-3-030-35288-2_1"},{"issue":"6419","key":"10.3233\/JIFS-201726_ref26","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1126\/science.aar6404","article-title":"A general reinforcement learning algorithm that masters chess, shogi, and go through self-play","volume":"362","author":"Silver","year":"2018","journal-title":"Science"},{"key":"10.3233\/JIFS-201726_ref27","doi-asserted-by":"crossref","unstructured":"He K. , Zhang X. , Ren S. and Sun J. , Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition (2016), 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.3233\/JIFS-201726_ref28","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1117\/12.474903","article-title":"Modeling soft factors in computer-based wargames","volume":"4716","author":"Alexander","year":"2002","journal-title":"Enabling Technologies for Simulation Science VI"},{"issue":"1","key":"10.3233\/JIFS-201726_ref29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2018.04.032","article-title":"Distributed simulation: state-of-the-art and potential for operational research","volume":"273","author":"Taylor","year":"2019","journal-title":"European Journal of Operational Research"},{"key":"10.3233\/JIFS-201726_ref31","unstructured":"Hessel M. , Modayil J. , Van Hasselt H. , Schaul T. , Ostrovski G. , Dabney W. , Horgan D. , Piot B. , Azar M. and Silver D. , Rainbow: Combining improvements in deep reinforcement learning, in Thirty-Second AAAI Conference on Artificial Intelligence, (2018)."},{"issue":"7676","key":"10.3233\/JIFS-201726_ref33","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1038\/nature24270","article-title":"Mastering the game of go without human knowledge","volume":"550","author":"Silver","year":"2017","journal-title":"Nature"},{"key":"10.3233\/JIFS-201726_ref36","first-page":"132","article-title":"Mining of weapon utility based on the replay data of war-game","volume":"2","author":"Xing","year":"2020","journal-title":"Journal of Command and Control"},{"key":"10.3233\/JIFS-201726_ref37","doi-asserted-by":"crossref","unstructured":"Pan Y. , Ni W. and Yang Y. , An algorithm to estimate enemy\u2019s location in wargame based on pheromone, in 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC), (2018).","DOI":"10.1109\/YAC.2018.8406471"},{"issue":"4","key":"10.3233\/JIFS-201726_ref39","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Computation"},{"issue":"8","key":"10.3233\/JIFS-201726_ref41","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Computation"},{"key":"10.3233\/JIFS-201726_ref43","doi-asserted-by":"crossref","unstructured":"Justesen N. and Risi S. , Learning macromanagement in starcraft from replays using deep learning, in 2017 IEEE Conference on Computational Intelligence and Games (CIG). IEEE, (2017), 162\u2013169.","DOI":"10.1109\/CIG.2017.8080430"},{"key":"10.3233\/JIFS-201726_ref45","doi-asserted-by":"crossref","unstructured":"Coulom R. , Efficient selectivity and backup operators in monte-carlo tree search, in International conference on computers and games. Springer, (2006), 72\u201383.","DOI":"10.1007\/978-3-540-75538-8_7"},{"key":"10.3233\/JIFS-201726_ref46","doi-asserted-by":"crossref","unstructured":"Mikolov T. , Karafi\u00e1t M. , Burget L. , \u010cernock\u1ef3 J. and Khudanpur S. , Recurrent neural network based language model, in Eleventh annual conference of the international speech communication association, (2010).","DOI":"10.1109\/ICASSP.2011.5947611"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-201726","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:31Z","timestamp":1777455691000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-201726"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,22]]},"references-count":28,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jifs-201726","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,22]]}}}