Kenneth Hullett

Design patterns in FPS levels

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.

Scenario generation for emergency rescue training games

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.

Data analytics for game development (NIER track)

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.

Extending neuro-evolutionary preference learning through player modeling

In this paper we propose a methodology for improving the accuracy of models that predict self-reported player pairwise preferences. Our approach extends neuro-evolutionary preference learning by embedding a player modeling module for the prediction of player preferences. Player types are identified using self-organization and feed the preference learner. Our experiments on a dataset derived from a game survey of subjects playing a 3D prey/predator game demonstrate that the player model-driven preference learning approach proposed improves the performance of preference learning significantly and shows promise for the construction of more accurate cognitive and affective models.

Empirical analysis of user data in game software 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 short-term 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.

Weapon design patterns in shooter games

With the increasing complexity of interactive games, the need for a common vocabulary to describe patterns grows. Action-oriented games focus heavily on players using weapons, but categorizations used for weapons are borrowed from real-world patterns, classifying them according to the physical inner-workings of the weapons rather than their gameplay effects. This terminology draws lines between similar weapons that elicit similar gameplay and creates unnecessary distinctions between fictional and non-fictional weapons. We aim to classify weapons in a way that defines weapons by the gameplay behaviors they elicit using a language of common weapon design patterns. This paper expands the taxonomy of game design patterns to better describe how weapons can be used to influence gameplay. These patterns act as a language for communicating game design concepts.

Enemy NPC design patterns in shooter games

Gameplay in single-player shooter games primarily consists of combat with Non-player Characters (NPCs). However, there has been little research done to study how enemy NPCs affect player behavior in shooter games. As a consequence, there is no formal language for designers to discuss how NPCs are used in shooter games. This paper presents design patterns for NPCs in shooter games and explores their effects on gameplay. These patterns help designers communicate about and explore new ideas for enemy NPCs and further our understanding about how NPC types can be implemented, enabling more engaging experiences.

The science of level design

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. We have taken a first step in improving understanding by identifying design patterns in first-person shooter (FPS) levels, providing cause-effect relationships between level design elements and gameplay. The next step is to validate this theory with a series of experiments that test the strength of these relationships. Analysis data collected from subjects can improve understanding and provide designers with scientifically verified tools for creating gameplay in FPS levels.