{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T15:58:12Z","timestamp":1774627092905,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>Generating diverse behaviors for game artificial intelligence (Game AI) has been long recognized as a challenging task in the game industry. Designing a Game AI with a satisfying behavioral characteristic (style) heavily depends on the domain knowledge and is hard to achieve manually. Deep reinforcement learning sheds light on advancing the automatic Game AI design. However, most of them focus on creating a superhuman Game AI, ignoring the importance of behavioral diversity in games. To bridge the gap, we introduce a new framework, named EMOGI, which can automatically generate desirable styles with almost no domain knowledge. More importantly, EMOGI succeeds in creating a range of diverse styles, providing behavior-diverse Game AIs. Evaluations on the Atari and real commercial games indicate that, compared to existing algorithms, EMOGI performs better in generating diverse behaviors and significantly improves the efficiency of Game AI design.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/466","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"3371-3377","source":"Crossref","is-referenced-by-count":22,"title":["Generating Behavior-Diverse Game AIs with Evolutionary Multi-Objective Deep Reinforcement Learning"],"prefix":"10.24963","author":[{"given":"Ruimin","family":"Shen","sequence":"first","affiliation":[{"name":"Fuxi Lab, NetEase"}]},{"given":"Yan","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University"},{"name":"Nanyang Technological University"}]},{"given":"Jianye","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University"},{"name":"Noah\u2019s Ark Lab, Huawei"},{"name":"Tianjin Key Lab of Machine Learning"}]},{"given":"Zhaopeng","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University"}]},{"given":"Yingfeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Fuxi Lab, NetEase"}]},{"given":"Changjie","family":"Fan","sequence":"additional","affiliation":[{"name":"Fuxi Lab, NetEase"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanyang Technological University"},{"name":"Institute of Computing Innovation, Zhejiang University"}]}],"member":"10584","event":{"name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","theme":"Artificial Intelligence","location":"Yokohama, Japan","acronym":"IJCAI-PRICAI-2020","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2020,7,11]]},"end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:15:19Z","timestamp":1594260919000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/466"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/466","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}