5 citations · 6 across the 8 of their papers we have counts for
8 papers
XSQ-AST: An Explainable Audio Spectrogram Transformer Framework for Localising Synthetic Speech Artifacts
Ben Heritage, Luca Resti, Mónica Villanueva Aylagas +3
Localising artifacts in synthetic speech remains challenging, as most evaluation methods yield only global quality scores. This paper presents XSQ-AST, a framework that combines th…
Using Deep Convolutional Neural Networks to Detect Rendered Glitches in Video Games
Carlos Garcia Ling, Konrad Tollmar, Linus Gisslen
In this paper, we present a method using Deep Convolutional Neural Networks (DCNNs) to detect common glitches in video games. The problem setting consists of an image (800x800 RGB)…
Generating Personas for Games with Multimodal Adversarial Imitation Learning
William Ahlberg, Alessandro Sestini, Konrad Tollmar +1
Reinforcement learning has been widely successful in producing agents capable of playing games at a human level. However, this requires complex reward engineering, and the agent's…
Automatic Testing and Validation of Level of Detail Reductions Through Supervised Learning
Matilda Tamm, Olivia Shamon, Hector Anadon Leon +2
Modern video games are rapidly growing in size and scale, and to create rich and interesting environments, a large amount of content is needed. As a consequence, often several thou…
CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories
Alessandro Sestini, Linus Gisslén, Joakim Bergdahl +2
This paper proposes a novel deep reinforcement learning algorithm to perform automatic analysis and detection of gameplay issues in complex 3D navigation environments. The Curiosit…
Augmenting Automated Game Testing with Deep Reinforcement Learning
Joakim Bergdahl, Camilo Gordillo, Konrad Tollmar +1
General game testing relies on the use of human play testers, play test scripting, and prior knowledge of areas of interest to produce relevant test data. Using deep reinforcement…
Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents
Camilo Gordillo, Joakim Bergdahl, Konrad Tollmar +1
As modern games continue growing both in size and complexity, it has become more challenging to ensure that all the relevant content is tested and that any potential issue is prope…
Adversarial Reinforcement Learning for Procedural Content Generation
Linus Gisslén, Andy Eakins, Camilo Gordillo +2
We present a new approach ARLPCG: Adversarial Reinforcement Learning for Procedural Content Generation, which procedurally generates and tests previously unseen environments with a…