{"207401":{"#nid":"207401","#data":{"type":"news","title":"Georgia Tech Uses \u0027Big Data\u0027 Algorithm to Customize Video Game Difficulty","body":[{"value":"\u003Cp\u003EGeorgia Tech researchers have developed a computational model that can predict video game players\u2019 in-game performance and provide a corresponding challenge they can beat, leading to quicker mastery of new skills. The advance not only could help improve user experiences with video games but also applications beyond the gaming world.\u003C\/p\u003E\u003Cp\u003EDigital gaming has surged in recent years and is being adopted almost as fast as the mobile devices that are enabling its growth. The Georgia Tech researchers developed a simple turn-based game, then used participant scores to apply algorithms that predict how others with similar skill sets would perform and adjust the difficulty accordingly.\u003C\/p\u003E\u003Cp\u003E\u201cPeople come in playing video games with different skills, abilities, interests and even desires, which is very contrary to the way video games are built now with a \u2018one size fits many approach,\u2019\u201d said Mark Riedl, co-creator of the model and assistant professor in the School of Interactive Computing.\u003C\/p\u003E\u003Cp\u003EThe researchers used a method called collaborative filtering, a popular technique employed by Netflix and Amazon in product ratings and recommendations. While Netflix recommends movies, the gaming model recommends the next challenge for players, adjusting game difficulty by computationally forecasting in-game performance. Riedl said the approach can scale to tens of thousands of users.\u003C\/p\u003E\u003Cp\u003EThe data-driven gaming model outperforms other current techniques specifically because it models player improvement over time, said Riedl. It uses an off-the-shelf algorithm, called tensor factorization, for the first time in gaming research to tailor challenges to individual players.\u003C\/p\u003E\u003Cp\u003EThe gaming model also includes a performance arc with which an algorithm selects in-game events for gamers that brings the predicted player performance in line with the developer\u2019s specifications for target performance (i.e., completing the game). Current games use player progress to make small adjustments to what\u2019s going on in the game, sometimes called \u201crubberbanding.\u201d The classic example: fall behind in a racing game and the other cars slow down; blow away the field with a large lead and the cars speed up.\u003C\/p\u003E\u003Cp\u003E\u201cThis is very reactionary,\u201d said Riedl, who directs the Georgia Tech Entertainment Intelligence Lab. \u201cYou have to wait for things to fall apart, and then the game tries to correct it in this ad-hoc way.\u201d\u003C\/p\u003E\u003Cp\u003ERiedl said that the new gaming model, which grows alongside the learner, has significant potential for educational and training applications as well. Students struggling with math concepts, for example, could use the model to master arithmetic and mitigate the chances of falling behind in a course, said Riedl.\u003C\/p\u003E\u003Cp\u003E\u201cWe\u2019ve also done some work with the U.S. Army,\u201d he said, \u201cto generate virtual missions where we choose and tailor the types of things that have to happen in the mission so that we don\u2019t overwhelm the novices or that we can really challenge the experts.\u201d\u003C\/p\u003E\u003Cp\u003E\u201cOur approach could allow novices to progress slowly and prevent them from abandoning a challenge right away,\u201d said Riedl. \u201cFor those good at certain skills, the game can be tuned to their particular talents to provide the right challenge at the right time.\u201d\u003C\/p\u003E\u003Cp\u003EAlex Zook, a Ph.D. candidate in human-centered computing, said that they were able to predict, with up to 93 percent accuracy, how players would perform in-game by modeling the changes in a player\u2019s skills and applying the recommendation algorithm.\u003C\/p\u003E\u003Cp\u003EZook was primary author on the paper he and Riedl presented on their findings at the \u003Ca href=\u0022http:\/\/aiide.org\/\u0022\u003E8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment\u003C\/a\u003E in Palo Alto, Calif. The paper is available at \u003Ca href=\u0022http:\/\/www.cc.gatech.edu\/~riedl\/pubs\/aiide12.pdf\u0022\u003Ehttp:\/\/www.cc.gatech.edu\/~riedl\/pubs\/aiide12.pdf\u003C\/a\u003E.\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":[{"value":"New model could be applied to training, educational content"}],"field_summary":[{"value":"\u003Cp\u003EGeorgia Tech researchers have developed a computational model that can predict video game players\u2019 in-game performance and provide a corresponding challenge they can beat, leading to quicker mastery of new skills.\u0026nbsp;The researchers used a method called collaborative filtering, a popular technique employed by Netflix and Amazon in product ratings and recommendations. While Netflix recommends movies, the gaming model recommends the next challenge for players, adjusting game difficulty by computationally forecasting in-game performance.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Researchers have developed a computational model that can lead to quicker mastery of new skills."}],"uid":"27592","created_gmt":"2013-04-18 08:29:46","changed_gmt":"2016-10-08 03:14:04","author":"Joshua Preston","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2013-04-18T00:00:00-04:00","iso_date":"2013-04-18T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"207461":{"id":"207461","type":"image","title":"Role-playing Game for Challenge Tailoring","body":null,"created":"1449179988","gmt_created":"2015-12-03 21:59:48","changed":"1475894866","gmt_changed":"2016-10-08 02:47:46","alt":"Role-playing Game for Challenge Tailoring","file":{"fid":"196778","name":"turn-based_role-playing_game.png","image_path":"\/sites\/default\/files\/images\/turn-based_role-playing_game_0.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/images\/turn-based_role-playing_game_0.png","mime":"image\/png","size":1801028,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/images\/turn-based_role-playing_game_0.png?itok=J6AL3Eaf"}},"50384":{"id":"50384","type":"image","title":"Mark Riedl","body":null,"created":"1449175392","gmt_created":"2015-12-03 20:43:12","changed":"1475894458","gmt_changed":"2016-10-08 02:40:58","alt":"Mark Riedl","file":{"fid":"128682","name":"mark-riedl.jpg","image_path":"\/sites\/default\/files\/images\/mark-riedl_1.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/images\/mark-riedl_1.jpg","mime":"image\/jpeg","size":12265,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/images\/mark-riedl_1.jpg?itok=NlCFZ53t"}}},"media_ids":["207461","50384"],"groups":[{"id":"1214","name":"News Room"}],"categories":[{"id":"42941","name":"Art Research"},{"id":"143","name":"Digital Media and Entertainment"}],"keywords":[{"id":"64411","name":"challenge tailoring"},{"id":"2356","name":"gaming"},{"id":"2449","name":"video games"}],"core_research_areas":[{"id":"39501","name":"People and Technology"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EJoshua Preston\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022mailto:jpreston@cc.gatech.edu\u0022\u003Ejpreston@cc.gatech.edu\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E678-231-0787\u003C\/p\u003E","format":"limited_html"}],"email":["jpreston@cc.gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}