Learning to Identify Perceptual Bugs in 3D Video Games

• URL: http://arxiv.org/abs/2202.12884v1
• Date: Fri, 25 Feb 2022 18:50:11 GMT
• Title: Learning to Identify Perceptual Bugs in 3D Video Games
• Authors: Benedict Wilkins, Kostas Stathis
• Abstract summary: We show that it is possible to identify a range of perceptual bugs using learning-based methods. World of Bugs (WOB) is an open platform for testing ABD methods in 3D game environments.
• Score: 1.370633147306388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated Bug Detection (ABD) in video games is composed of two distinct but complementary problems: automated game exploration and bug identification. Automated game exploration has received much recent attention, spurred on by developments in fields such as reinforcement learning. The complementary problem of identifying the bugs present in a player's experience has for the most part relied on the manual specification of rules. Although it is widely recognised that many bugs of interest cannot be identified with such methods, little progress has been made in this direction. In this work we show that it is possible to identify a range of perceptual bugs using learning-based methods by making use of only the rendered game screen as seen by the player. To support our work, we have developed World of Bugs (WOB) an open platform for testing ABD methods in 3D game environments.

Related papers

• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Deriving and Evaluating a Detailed Taxonomy of Game Bugs [2.2136561577994858]
  The goal of this work is to provide a bug taxonomy for games that will help game developers in developing bug-resistant games. We performed a Multivocal Literature Review (MLR) by analyzing 436 sources, out of which 189 (78 academic and 111 grey) sources reporting bugs encountered in the game development industry were selected for analysis. The MLR allowed us to finalize a detailed taxonomy of 63 game bug categories in end-user perspective.
  arXiv  Detail & Related papers  (2023-11-28T09:51:42Z)
• Finding the Needle in a Haystack: Detecting Bug Occurrences in Gameplay Videos [10.127506928281413]
  We present an automated approach that uses machine learning to predict whether a segment of a gameplay video contains a depiction of a bug. We analyzed 4,412 segments of 198 gameplay videos to predict whether a segment contains an instance of a bug. Our approach is effective at detecting segments of a video that contain bugs, achieving a high F1 score of 0.88, outperforming the current state-of-the-art technique for bug classification.
  arXiv  Detail & Related papers  (2023-11-18T01:14:18Z)
• Towards Long-Tailed 3D Detection [56.82185415482943]
  We study the problem of Long-Tailed 3D Detection (LT3D), which evaluates on all classes, including those in-the-tail. Our modifications improve accuracy by 5% AP on average for all classes, and dramatically improve AP for rare classes.
  arXiv  Detail & Related papers  (2022-11-16T06:00:47Z)
• Large Language Models are Pretty Good Zero-Shot Video Game Bug Detectors [3.39487428163997]
  We show that large language models can identify which event is buggy in a sequence of textual descriptions of events from a game. Our results show promising results for employing language models to detect video game bugs.
  arXiv  Detail & Related papers  (2022-10-05T18:44:35Z)
• Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation [116.41186277555386]
  Inspector is a game testing agent that can be easily applied to different games without deep integration with games. Inspector is based on purely pixel inputs and comprises three key modules: game space explorer, key object detector, and human-like object investigator. Experiment results demonstrate the effectiveness of Inspector in exploring game space, detecting key objects, and investigating objects.
  arXiv  Detail & Related papers  (2022-07-18T04:49:07Z)
• World of Bugs: A Platform for Automated Bug Detection in 3D Video Games [1.370633147306388]
  We present World of Bugs, an open platform that aims to support Automated Bug Detection research in video games. Key feature is a growing collection of common video game bugs that may be used for training and evaluating ABD approaches.
  arXiv  Detail & Related papers  (2022-06-21T10:52:03Z)
• CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories [65.35714948506032]
  The Curiosity-Conditioned Proximal Trajectories (CCPT) method combines curiosity and imitation learning to train agents to explore. We show how CCPT can explore complex environments, discover gameplay issues and design oversights in the process, and recognize and highlight them directly to game designers.
  arXiv  Detail & Related papers  (2022-02-21T09:08:33Z)
• Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving [87.3492357041748]
  In this paper, we showcase practical susceptibilities of multi-sensor detection by placing an adversarial object on top of a host vehicle. Our experiments demonstrate that successful attacks are primarily caused by easily corrupted image features. Towards more robust multi-modal perception systems, we show that adversarial training with feature denoising can boost robustness to such attacks significantly.
  arXiv  Detail & Related papers  (2021-01-17T21:15:34Z)
• Anomaly Detection in Video via Self-Supervised and Multi-Task Learning [113.81927544121625]
  Anomaly detection in video is a challenging computer vision problem. In this paper, we approach anomalous event detection in video through self-supervised and multi-task learning at the object level.
  arXiv  Detail & Related papers  (2020-11-15T10:21:28Z)
• A Metric Learning Approach to Anomaly Detection in Video Games [1.1602089225841632]
  We develop State-State Siamese Networks (S3N) as an efficient deep metric learning approach to anomaly detection. We show by empirical evaluation on a series of Atari games, that S3N is able to learn a meaningful embedding.
  arXiv  Detail & Related papers  (2020-05-20T17:23:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.