Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/47532
Title: | Pairing character classes in a deathmatch shooter game via a deep-learning surrogate model |
Authors: | Karavolos, Daniel Liapis, Antonios Yannakakis, Georgios N. |
Keywords: | Computer games -- Design Artificial intelligence Evolutionary computation Neural networks (Computer science) |
Issue Date: | 2018 |
Publisher: | Association for Computing Machinery |
Citation: | Karavolos, D., Liapis, A., & Yannakakis, G. N. (2018). Pairing character classes in a deathmatch shooter game via a deep-learning surrogate model. Proceedings of the FDG Workshop on Procedural Content Generation, Malmo. |
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 levels. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/47532 |
Appears in Collections: | Scholarly Works - InsDG |
Files in This Item:
File | Description | Size | Format | |
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Pairing_character_classes_in_a_deathmatch_shooter_game_via_a_deep-learning_surrogate_model_2018.pdf | 1.11 MB | Adobe PDF | View/Open |
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