Data-Driven Approaches to Game Player Modeling: A Systematic Literature Review
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hooshyar, Danial | - |
dc.contributor.author | Yousefi, Moslem | - |
dc.contributor.author | Lim, Heuiseok | - |
dc.date.accessioned | 2021-09-02T17:16:36Z | - |
dc.date.available | 2021-09-02T17:16:36Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2018-01 | - |
dc.identifier.issn | 0360-0300 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/78547 | - |
dc.description.abstract | Modeling and predicting player behavior is of the utmost importance in developing games. Experience has proven that, while theory-driven approaches are able to comprehend and justify a model's choices, such models frequently fail to encompass necessary features because of a lack of insight of the model builders. In contrast, data-driven approaches rely much less on expertise, and thus offer certain potential advantages. Hence, this study conducts a systematic review of the extant research on data-driven approaches to game player modeling. To this end, we have assessed experimental studies of such approaches over a nine-year period, from 2008 to 2016; this survey yielded 46 research studies of significance. We found that these studies pertained to three main areas of focus concerning the uses of data-driven approaches in game player modeling. One research area involved the objectives of data-driven approaches in game player modeling: behavior modeling and goal recognition. Another concerned methods: classification, clustering, regression, and evolutionary algorithm. The third was comprised of the current challenges and promising research directions for data-driven approaches in game player modeling. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ASSOC COMPUTING MACHINERY | - |
dc.subject | RECOGNITION | - |
dc.subject | EXPERIENCE | - |
dc.title | Data-Driven Approaches to Game Player Modeling: A Systematic Literature Review | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lim, Heuiseok | - |
dc.identifier.doi | 10.1145/3145814 | - |
dc.identifier.scopusid | 2-s2.0-85040162683 | - |
dc.identifier.wosid | 000419881700013 | - |
dc.identifier.bibliographicCitation | ACM COMPUTING SURVEYS, v.50, no.6 | - |
dc.relation.isPartOf | ACM COMPUTING SURVEYS | - |
dc.citation.title | ACM COMPUTING SURVEYS | - |
dc.citation.volume | 50 | - |
dc.citation.number | 6 | - |
dc.type.rims | ART | - |
dc.type.docType | Review | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.subject.keywordPlus | EXPERIENCE | - |
dc.subject.keywordAuthor | Game player modeling | - |
dc.subject.keywordAuthor | data-driven approaches | - |
dc.subject.keywordAuthor | computational models | - |
dc.subject.keywordAuthor | systematic literature review (SLR) | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.