Level designers create gameplay through geometry, AI scripting, and item placement. There is little formal understanding of this process, but rather a large body of design lore and rules of thumb. As a result, there is no accepted common language for describing the building blocks of level design and the gameplay they create. This paper presents level design patterns for first-person shooter (FPS) games, providing cause-effect relationships between level design elements and gameplay. These patterns allow designers to create more interesting and varied levels.

Procedural methods have long been used for generation of art assets, but procedural generation of scenarios has lagged behind. In particular, training games for emergency rescue workers would benefit from procedural scenario generation guided by pedagogical goals. In such a game, users could select what skills they wish to train for, and the system would generate a unique level containing the elements necessary to train those skills. In this paper, we present a system that uses HTN planning to generate collapsed structure training scenarios that are both internally consistent and allow the user to train for the desired goals.

The software engineering community has had seminal papers on data analysis for software productivity, quality, reliability, performance etc. Analyses have involved software systems ranging from desktop software to telecommunication switching systems. Little work has been done on the emerging digital game industry. In this paper we explore how data can drive game design and production decisions in game development. We define a mixture of qualitative and quantitative data sources, broken down into three broad categories: internal testing, external testing, and subjective evaluations. We present preliminary results of a case study of how data collected from users of a released game can inform subsequent development.

For several years empirical studies have spanned the spectrum of research from software productivity, quality, reliability, performance to human computer interaction. Analyses have involved software systems ranging from desktop software to telecommunication switching systems. But surprising there has been little work done on the emerging digital game industry, one of the fastest growing domains today. To the best of our knowledge, our work is one of the first empirical analysis of a large commercially successful game system. In this paper, we introduce an analysis of the significant user data generated in the gaming industry by using a successful game: Project Gotham Racing 4.More specifically, due to the increasing ubiquity of constantly connected high-speed internet connections for game consoles, developers are able to collect extensive amounts of data about their games following release. The challenge now is to make sense of that data, and from it be able to make recommendations to developers. This paper presents an empirical case study analyzing the data collected from a released game over a three year period. The results of this analysis include a better understanding of the differences between long-term and shortterm players, and the extent to which various options in the game are utilized. This led to recommendations for future development ways to reduce development costs and to keep new players engaged. A secondary goal for this paper is to introduce software game development as a topic of importance to the empirical software engineering community and discuss research results on a key difference area: data analytics on user data to customize user and development experiences.

No abstract