{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T16:39:23Z","timestamp":1779122363025,"version":"3.51.4"},"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":[[2017,8]]},"abstract":"<jats:p>Data-driven techniques for interactive narrative generation are the subject of growing interest. Reinforcement learning (RL) offers significant potential for devising data-driven interactive narrative generators that tailor players\u2019 story experiences by inducing policies from player interaction logs. A key open question in RL-based interactive narrative generation is how to model complex player interaction patterns to learn effective policies. In this paper we present a deep RL-based interactive narrative generation framework that leverages synthetic data produced by a bipartite simulated player model. Specifically, the framework involves training a set of Q-networks to control adaptable narrative event sequences with long short-term memory network-based simulated players. We investigate the deep RL framework\u2019s performance with an educational interactive narrative, Crystal Island. Results suggest that the deep RL-based narrative generation framework yields effective personalized interactive narratives.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/538","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T09:14:07Z","timestamp":1501233247000},"page":"3852-3858","source":"Crossref","is-referenced-by-count":27,"title":["Interactive Narrative Personalization with Deep Reinforcement Learning"],"prefix":"10.24963","author":[{"given":"Pengcheng","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Computer Science, North Carolina State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Rowe","sequence":"additional","affiliation":[{"name":"Department of Computer Science, North Carolina State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wookhee","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Computer Science, North Carolina State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bradford","family":"Mott","sequence":"additional","affiliation":[{"name":"Department of Computer Science, North Carolina State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Lester","sequence":"additional","affiliation":[{"name":"Department of Computer Science, North Carolina State University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"IJCAI-2017","number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"start":{"date-parts":[[2017,8,19]]},"end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T11:54:25Z","timestamp":1501242865000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/538"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/538","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}