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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


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Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες Except where otherwise noted, this item's license is described as Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες