{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T12:44:54Z","timestamp":1771677894193,"version":"3.50.1"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,17]]},"DOI":"10.1109\/cog52621.2021.9619162","type":"proceedings-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T15:53:06Z","timestamp":1638892386000},"page":"1-7","source":"Crossref","is-referenced-by-count":15,"title":["DL-DDA - Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints"],"prefix":"10.1109","author":[{"given":"Dvir Ben","family":"Or","sequence":"first","affiliation":[]},{"given":"Michael","family":"Kolomenkin","sequence":"additional","affiliation":[]},{"given":"Gil","family":"Shabat","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3041021.3054170"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/SeGAH.2017.7939260"},{"key":"ref12","article-title":"AI for dynamic difficulty adjustment in games","volume":"2","author":"hunicke","year":"2004","journal-title":"Challenges in Game Artificial Intelligence AAAI Workshop"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/1178477.1178573"},{"key":"ref14","first-page":"61","article-title":"Making Racing Fun Through Player Modeling and Track Evolution","volume":"2","author":"togelius","year":"2006","journal-title":"Optimizing Player Satisfaction in Computer and Physical Games"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.07.563"},{"key":"ref16","first-page":"20","article-title":"A Temporal Data-Driven Player Model for Dynamic Difficulty Adjustment","author":"zook","year":"2012","journal-title":"Eighth Artificial Intelligence and Interactive Digital Entertainment Conference"},{"key":"ref17","first-page":"8","article-title":"Games with Dynamic Difficulty Adjustment using POMDPs","author":"goetschalckx","year":"2010","journal-title":"ICML Workshop"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/1015530.1015549"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00401"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2012.03.004"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.4444"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1590\/S0101-82052003000100003","article-title":"On the convergence properties of the projected gradient method for convex optimization","volume":"22","author":"iusem","year":"2003","journal-title":"Computational & Applied Mathematics"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1162\/089976604773135104"},{"key":"ref8","first-page":"39","article-title":"Adjusting the Difficulty of Running Game with Facial Expression Recognition Technology Using Convolutional Neural Network","volume":"31","author":"wang","year":"2018","journal-title":"KoreaScholar-journal of information systems"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.entcom.2017.11.003"},{"key":"ref2","author":"koster","year":"2004","journal-title":"A Theory of Fun for Game Design"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1080\/08839510701527580"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/5681652"},{"key":"ref20","first-page":"1","volume":"1","author":"grabocka","year":"2019","journal-title":"Learning Surrogate Losses"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.13001\/1081-3810.1551"},{"key":"ref21","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics United States Journal of Machine Learning Research"},{"key":"ref24","author":"or","year":"2020","journal-title":"Generalized Quantile Loss for Deep Neural Networks"},{"key":"ref23","first-page":"11427","article-title":"Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks","author":"fazlyab","year":"2019","journal-title":"Advances in neural information processing systems"}],"event":{"name":"2021 IEEE Conference on Games (CoG)","location":"Copenhagen, Denmark","start":{"date-parts":[[2021,8,17]]},"end":{"date-parts":[[2021,8,20]]}},"container-title":["2021 IEEE Conference on Games (CoG)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9618888\/9618891\/09619162.pdf?arnumber=9619162","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T12:53:35Z","timestamp":1652187215000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9619162\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,17]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/cog52621.2021.9619162","relation":{},"subject":[],"published":{"date-parts":[[2021,8,17]]}}}