Journal of Game Development

GENERATING GAME AUDIO BY EXAMPLE
J. R. Parker, Brad Behm, Sonny Chan

ABSTRACT

When creating multimedia constructions such as Web pages, games, and animations, the use of appropriate sounds can convey both information and mood. It is common to play some type of sound as a “loop,” where the sound file is played repeatedly from beginning to end. This is an easy way to extend a short audio sample, but the repetition quickly becomes irritating. Based on previous work in computer graphics on texture synthesis, it is possible to create new sounds from examples, so that looping is not needed. This works best, although not perfectly, for sound textures, which provide a background sound with few identifiable components (e.g., rainfall, waves), and works reasonably well for a wider variety of sounds.

AUTHORING EMERGENT NARRATIVE-BASED GAMES
Sandy Louchart, Michael Kriegel, Rui Figueiredo, Ana Paiva

ABSTRACT

In this article, we address the particular issue of authoring interactive narrative with respect to video games and interactive storytelling. We first introduce the narrative paradox between interactivity and narrative content in virtual environments and consider its impact on game design and development. We then introduce the concept of the Emergent Narrative (EN) and the particular philosophy it has been developed upon. Finally, we describe an authoring process for this approach that reflects on the characteristics of interacting within such a narrative framework.

THE RESTAURANT GAME: LEARNING SOCIAL BEHAVIOR AND LANGUAGE FROM THOUSANDS OF PLAYERS ONLINE
Jeff Orkin and Deb Roy

ABSTRACT

We envision a future in which conversational virtual agents collaborate with humans in games and training simulations. A representation of common ground for everyday scenarios is essential for these agents if they are to be effective collaborators and communicators. Effective collaborators can infer a partner’s goals and predict future actions. Effective communicators can infer the meaning of utterances based on semantic context. This article introduces a computational model of common ground called a Plan Network, a statistical model that encodes context-sensitive expected patterns of behavior and language, with dependencies on social roles and object affordances. We describe a methodology for unsupervised learning of a Plan Network using a multiplayer video game, visualization of this network, and evaluation of the learned model with respect to human judgment of typical behavior. Specifically, we describe learning the Restaurant Plan Network from data collected from over 5,000 gameplay sessions of a minimal investment multiplayer online (MIMO) role-playing game called The Restaurant Game. Our results demonstrate a kind of social common sense for virtual agents, and have implications for automatic authoring of content in the future.