Measuring Visual Consistency in 3D Rendering Systems

One of the major challenges facing a present day game development company is the removal of bugs from such complex virtual environments. This work presents an approach for measuring the correctness of synthetic scenes generated by a rendering system of a 3D application, such as a computer game. Our approach builds a database of labelled point clouds representing the spatiotemporal colour distribution for the objects present in a sequence of bug-free frames. This is done by converting the position that the pixels take over time into the 3D equivalent points with associated colours. Once the space of labelled points is built, each new image produced from the same game by any rendering system can be analysed by measuring its visual inconsistency in terms of distance from the database. Objects within the scene can be relocated (manually or by the application engine); yet the algorithm is able to perform the image analysis in terms of the 3D structure and colour distribution of samples on the surface of the object. We applied our framework to the publicly available game RacingGame developed for Microsoft(R) Xna(R). Preliminary results show how this approach can be used to detect a variety of visual artifacts generated by the rendering system in a professional quality game engine

ANALYSIS OF ARTIFICIAL INTELLIGENCE APPLICATIONS FOR AUTOMATED TESTING OF VIDEO GAMES

Game testing is a software testing process for quality control in video games. Game environments, sometimes called levels or maps, are complex and interactive systems. These environments can include level geometry, interactive entities, player and non-player controllable characters etc. Depending on the number and complexity of levels, testing them by hand may take a considerable effort. This is especially true for video games with procedurally generated levels that are automatically created using a specifically designed algorithm. A single change in a procedural generation algorithm can alter all of the video game levels, and they will have to be retested to ensure they are still completable or meet any other requirements of the game. This task may be suitable for automation, in particular using Artificial Intelligence (AI). The goal of this paper is to explore the most promising and up-to-date research on AI applications for video game testing to serve as a reference for anyone starting in the field

Reinforcement learning for qualitative group behaviours applied to non-player computer game characters

This thesis investigates how to train the increasingly large cast of characters in modern commercial computer games. Modern computer games can contain hundreds or sometimes thousands of non-player characters that each should act coherently in complex dynamic worlds, and engage appropriately with other non-player characters and human players. Too often, it is obvious that computer controlled characters are brainless zombies portraying the same repetitive hand-coded behaviour. Commercial computer games would seem a natural domain for reinforcement learning and, as the trend for selling games based on better graphics is peaking with the saturation of game shelves with excellent graphics, it seems that better artificial intelligence is the next big thing. The main contribution of this thesis is a novel style of utility function, group utility functions, for reinforcement learning that could provide automated behaviour specification for large numbers of computer game characters. Group utility functions allow arbitrary functions of the characters’ performance to represent relationships between characters and groups of characters. These qualitative relationships are learned alongside the main quantitative goal of the characters. Group utility functions can be considered a multi-agent extension of the existing programming by reward method and, an extension of the team utility function to be more generic by replacing the sum function with potentially any other function. Hierarchical group utility functions, which are group utility functions arranged in a tree structure, allow character group relationships to be learned. For illustration, the empirical work shown uses the negative standard deviation function to create balanced (or equal performance) behaviours. This balanced behaviour can be learned between characters, groups and also, between groups and single characters. Empirical experiments show that a balancing group utility function can be used to engender an equal performance between characters, groups, and groups and single characters. It is shown that it is possible to trade some amount of quantitatively measured performance for some qualitative behaviour using group utility functions. Further experiments show how the results degrade as expected when the number of characters and groups is increased. Further experimentation shows that using function approximation to approximate the learners’ value functions is one possible way to overcome the issues of scale. All the experiments are undertaken in a commercially available computer game engine. In summary, this thesis contributes a novel type of utility function potentially suitable for training many computer game characters and, empirical work on reinforcement learning used in a modern computer game engine