Pairing Character 
Classes in a Deathmatch Shooter Game via a Deep-Learning Surrogate Model

dc.contributor.author	Karavolos, Daniel	en
dc.contributor.author	Liapis, Antonios	en
dc.contributor.author	Yannakakis, Georgios N.	en
dc.date.accessioned	2019-02-17T05:29:54Z
dc.date.available	2019-02-17T05:29:54Z
dc.date.issued	2019-02-17
dc.identifier.uri	http://hdl.handle.net/11400/20235
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	Pairing Character Classes in a Deathmatch Shooter Game via a Deep-Learning Surrogate Model	en
heal.type	conferenceItem
heal.identifier.secondary	http://antoniosliapis.com/papers/pairing_character_classes_in_a_deathmatch_shooter_game_via_a_deep-learning_surrogate_model.pdf
heal.language	en
heal.access	free
heal.publicationDate	2018
heal.bibliographicCitation	Daniel Karavolos, Antonios Liapis and Georgios N. Yannakakis: "Pairing Character Classes in a Deathmatch Shooter Game via a Deep-Learning Surrogate Model" in Proceedings of the FDG Workshop on Procedural Content Generation, 2018.	en
heal.abstract	This paper introduces a surrogate model of gameplay that learns the mapping between different game facets, and applies it to a generative system which designs new content in one of these facets.Focusing on the shooter game genre, the paper explores how deep learning can help build a model which combines the game level structure and the game’s character class parameters as input and the gameplay outcomes as output. The model is trained on a large corpus of game data from simulations with artificial agents in random sets of levels and class parameters. The model is then used to generate classes for specific levels and for a desired game outcome,such as balanced matches of short duration. Findings in this paper show that the system can be expressive and can generate classes for both computer generated and human authored level	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	Proceedings of the FDG Workshop on Procedural Content Generation	en
heal.conferenceItemType	full paper