2 research outputs found
Pairing character classes in a deathmatch shooter game via a deep-learning surrogate model
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.peer-reviewe
DATA Agent
This paper introduces DATA Agent, a system which creates murder
mystery adventures from open data. In the game, the player
takes on the role of a detective tasked with finding the culprit of
a murder. All characters, places, and items in DATA Agent games
are generated using open data as source content. The paper discusses
the general game design and user interface of DATA Agent,
and provides details on the generative algorithms which transform
linked data into different game objects. Findings from a user study
with 30 participants playing through two games of DATA Agent
show that the game is easy and fun to play, and that the mysteries
it generates are straightforward to solve.peer-reviewe