Orchestrating Game Generation

The design process is often characterized by and realized through the iterative steps of evaluation and refinement. When the process is based on a single creative domain such as visual art or audio production, designers primarily take inspiration from work within their domain and refine it based on their own intuitions or feedback from an audience of experts from within the same domain. What happens, however, when the creative process involves more than one creative domain such as in a digital game? How should the different domains influence each other so that the final outcome achieves a harmonized and fruitful communication across domains? How can a computational process orchestrate the various computational creators of the corresponding domains so that the final game has the desired functional and aesthetic characteristics? To address these questions, this article identifies game facet orchestration as the central challenge for AI-based game generation, discusses its dimensions and reviews research in automated game generation that has aimed to tackle it. In particular, we identify the different creative facets of games, we propose how orchestration can be facilitated in a top-down or bottom-up fashion, we review indicative preliminary examples of orchestration, and we conclude by discussing the open questions and challenges ahead

Explainable AI for designers: A human-centered perspective on mixed-initiative co-creation

Growing interest in eXplainable Artificial Intelligence (XAI) aims to make AI and machine learning more understandable to human users. However, most existing work focuses on new algorithms, and not on usability, practical interpretability and efficacy on real users. In this vision paper, we propose a new research area of eXplainable AI for Designers (XAID), specifically for game designers. By focusing on a specific user group, their needs and tasks, we propose a human-centered approach for facilitating game designers to co-create with AI/ML techniques through XAID. We illustrate our initial XAID framework through three use cases, which require an understanding both of the innate properties of the AI techniques and users' needs, and we identify key open challenges.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work publicComp Graphics & Visualisatio

Procedural Content Graphs for Urban Modeling

Massive procedural content creation, for example, for virtual urban environments, is a difficult, yet important challenge. While shape grammars are a popular example of effectiveness in architectural modeling, they have clear limitations regarding readability, manageability, and expressive power when addressing a variety of complex structural designs. Moreover, shape grammars aim at geometry specification and do not facilitate integration with other types of content, such as textures or light sources, which could rather accompany the generation process. We present procedural content graphs, a graph-based solution for procedural generation that addresses all these issues in a visual, flexible, and more expressive manner. Besides integrating handling of diverse types of content, this approach introduces collective entity manipulation as lists, seamlessly providing features such as advanced filtering, grouping, merging, ordering, and aggregation, essentially unavailable in shape grammars. Hereby, separated entities can be easily merged or just analyzed together in order to perform a variety of context-based decisions and operations. The advantages of this approach are illustrated via examples of tasks that are either very cumbersome or simply impossible to express with previous grammar approaches

Web-based collaborative feature modeling

Collaborative modeling systems are distributed multiple-user sys-tems that are both concurrent and synchronized, aimed at sup-porting engineering teams in coordinating their modeling activi-ties. Instead of an iterative process, asynchronously sending prod

Mixed-media game AI

Over the last decades, digital technologies have moved away from the personal computer (PC) into cloud computing, ubiquitous computing, intelligent robots and smart devices. From wearable technologies to remote-controlled household items and from sensors for crowd control to personal drones, there is a broad range of sources which can be exploited for artificial intelligence in games. While artificial intelligence (AI) is already a big part of the Internet of Things, raising concerns in terms of ethics and politics, games have been relatively partitioned away to PCs. Relevant work on wearable technologies as game controllers, mixed- or virtual-reality rendering, or technology-enhanced play in playgrounds, social robots for games or board games, has largely not taken advantage of artificial intelligence for controlling or mediating the experience. Using the term mixed-media to refer broadly to any media, digital or otherwise, outside the game data within a PC or a game-specific database, this working group attempted to map out the broad topic of mixed-media in terms of its applications for game AI.peer-reviewe

Story ARtist

Most content creation applications currently in use are conventional PC applications with visualisation on a 2D screen and indirect interaction, e.g. through mouse and keyboard. Augmented Reality (AR) is a medium that can provide actual 3D visualisation and more hands-on interaction for these applications, due to its technology adding virtual elements to a real-world environment. We explored how AR can be used for story authoring, a specific type of content creation, and investigated how both types of existing AR interfaces, tangible and touch-less, can be combined in a useful way in that context [1]. The Story ARtist application was developed to evaluate the designed interactions and AR visualisation for story authoring. It features a tabletop environment to dynamically visualise the story authoring elements, augmented by the 3D space that AR provides. Story authoring is kept simple, with a linear plot point structure focused on core story elements like actions, characters and objects. </p

Persistent Naming Through Persistent Entities

Abstrac