Using a Surrogate 
Model of Gameplay for Automated Level Design

dc.contributor.author	Karavolos, Daniel	en
dc.contributor.author	Liapis, Antonios	en
dc.contributor.author	Yannakakis, Georgios N.	en
dc.date.accessioned	2019-02-16T19:21:00Z
dc.date.available	2019-02-16T19:21:00Z
dc.date.issued	2019-02-16
dc.identifier.uri	http://hdl.handle.net/11400/20234
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.title	Using a Surrogate Model of Gameplay for Automated Level Design	en
heal.type	conferenceItem
heal.language	en
heal.access	free
heal.publicationDate	2018
heal.bibliographicCitation	Daniel Karavolos, Antonios Liapis and Georgios N. Yannakakis: "Using a Surrogate Model of Gameplay for Automated Level Design" in Proceedings of the IEEE Conference on Computational Intelligence and Games, 2018.	en
heal.abstract	This paper describes how a surrogate model of the interrelations between different types of content in the samegame 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 rules	en
heal.sponsor	The CROSSCULT project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 693150	en
heal.fullTextAvailability	false
heal.conferenceName	2018 IEEE Conference on Computational	en
heal.conferenceItemType	full paper