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DC Field | Value | Language |
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dc.contributor.author | Pfau, Johannes | - |
dc.contributor.author | Liapis, Antonios | - |
dc.contributor.author | Volkmar, Georg | - |
dc.contributor.author | Yannakakis, Georgios N. | - |
dc.contributor.author | Malaka, Rainer | - |
dc.date.accessioned | 2021-09-07T06:50:45Z | - |
dc.date.available | 2021-09-07T06:50:45Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Pfau, J., Liapis, A., Volkmar, G., Yannakakis, G. N., & Malaka, R. (2020, August). Dungeons & replicants: automated game balancing via deep player behavior modeling. In 2020 IEEE Conference on Games (CoG) (pp. 431-438). IEEE. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/80777 | - |
dc.description | This work was funded by the German Research Foundation (DFG) as part of Collaborative Research Center (SFB) 1320 EASE - Everyday Activity Science and Engineering, University of Bremen (http://www.easecrc.org/), subproject H2. | en_GB |
dc.description.abstract | Balancing the options available to players in a way that ensures rich variety and viability is a vital factor for the success of any video game, and particularly competitive multiplayer games. Traditionally, this balancing act requires extensive periods of expert analysis, play testing and debates. While automated gameplay is able to predict outcomes of parameter changes, current approaches mainly rely on heuristic or optimal strategies to generate agent behavior. In this paper, we demonstrate the use of deep player behavior models to represent a player population (n = 213) of the massively multiplayer online role-playing game Aion, which are used, in turn, to generate individual agent behaviors. Results demonstrate significant balance differences in opposing enemy encounters and show how these can be regulated. Moreover, the analytic methods proposed are applied to identify the balance relationships between classes when fighting against each other, reflecting the original developers’ design. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | IEEE | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Games -- Design | en_GB |
dc.subject | Computer games -- Design | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.subject | Video games -- Design | en_GB |
dc.title | Dungeons & replicants : automated game balancing via deep player behavior modeling | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | IEEE Conference on Games | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
Appears in Collections: | Scholarly Works - InsDG |
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Dungeons_amp_Replicants_Automated_Game_Balancing_via_Deep_Player_Behavior_Modeling.pdf Restricted Access | 5.57 MB | Adobe PDF | View/Open Request a copy |
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