Using a Surrogate Model of Gameplay forAutomated Level Design
| Main Authors: | Karavolos, Daniel, Liapis, Antonios, Yannakakis, Georgios N |
|---|---|
| Format: | Proceeding |
| Terbitan: |
, 2018
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| Online Access: |
https://zenodo.org/record/2567174 |
| ctrlnum |
2567174 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Karavolos, Daniel</creator><creator>Liapis, Antonios</creator><creator>Yannakakis, Georgios N</creator><date>2018-12-28</date><description>This paper describes how a surrogate model of the interrelations between different types of content in the same game can be used for level generation. Specifically, the model associates level structure and game rules with gameplay outcomes in a shooter game. We use a deep learning approach to train a model on simulated play throughs of two-player death match games, in diverse levels and with different character classes per player. Findings in this paper show that the model can predict the duration and winner of the match given a top-down map of the level and the parameters of the two players’ character classes. With this surrogate model in place, we investigate which level structures would result in a balanced match of short,medium or long duration for a given set of character classes.Using evolutionary computation, we are able to discover levels which improve the balance between different classes. This opens up potential applications for a designer tool which can adapt a human authored map to fit the designer’s desired gameplay outcomes, taking account of the game’s rule</description><identifier>https://zenodo.org/record/2567174</identifier><identifier>10.5281/zenodo.2567174</identifier><identifier>oai:zenodo.org:2567174</identifier><relation>info:eu-repo/grantAgreement/EC/H2020/693150/</relation><relation>doi:10.5281/zenodo.2567173</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><title>Using a Surrogate Model of Gameplay forAutomated Level Design</title><type>Journal:Proceeding</type><type>Journal:Proceeding</type><recordID>2567174</recordID></dc>
|
| format |
Journal:Proceeding Journal |
| author |
Karavolos, Daniel Liapis, Antonios Yannakakis, Georgios N |
| title |
Using a Surrogate Model of Gameplay forAutomated Level Design |
| publishDate |
2018 |
| url |
https://zenodo.org/record/2567174 |
| contents |
This paper describes how a surrogate model of the interrelations between different types of content in the same game can be used for level generation. Specifically, the model associates level structure and game rules with gameplay outcomes in a shooter game. We use a deep learning approach to train a model on simulated play throughs of two-player death match games, in diverse levels and with different character classes per player. Findings in this paper show that the model can predict the duration and winner of the match given a top-down map of the level and the parameters of the two players’ character classes. With this surrogate model in place, we investigate which level structures would result in a balanced match of short,medium or long duration for a given set of character classes.Using evolutionary computation, we are able to discover levels which improve the balance between different classes. This opens up potential applications for a designer tool which can adapt a human authored map to fit the designer’s desired gameplay outcomes, taking account of the game’s rule |
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2022-06-06T02:48:18Z |
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