Video game designers iteratively improve player experience by play testing game software and adjusting its design. Deciding how to improve gameplay is difficult and time-consuming because designers lack an effective means for exploring decision alternatives and modifying a game’s mechanics. We aim to improve designer productivity and game quality by providing tools that speed-up the game design process. In particular, we wish to learn how patterns en- coding common game design knowledge can help to improve design tools. Micro-Machinations (MM) is a language and software library that enables game designers to modify a game’s mechanics at run-time. We propose a pattern-based approach for leveraging high-level design knowledge and facilitating the game design process with a game design assistant. We present the Mechanics Pattern Language (MPL) for encoding common MM structures and design intent, and a Mechanics Design Assistant (MeDeA) for analyzing, explaining and understanding existing mechanics, and generating, filtering, exploring and applying design alternatives for modifying mechanics. We implement MPL and MeDeA using the meta-programming language Rascal, and evaluate them by modifying the mechanics of a prototype of Johnny Jetstream, a 2D shooter developed at IC3D Media.
This paper presents the latest version of the Machinations framework. This framework uses diagrams to represent the flow of tangible and abstract resources through a game. This flow represents the mechanics that make up a game’s interbal economy and has a large impact on the emergent gameplay of most simulation games, strategy games and board games. This paper shows how Machinations diagrams can be used simulate and balance games before they are built.
© Springer International Publishing AG 2016. A serious game needs to combine a number of different aspects to help the end user in reaching the desired effects. This requires incorporating a broad range of different aspects in the design, stemming from a broad range of different fields of expertise. For designers, developers, researchers, and other stakeholders it is not straightforward how to organize the design and development process, to make sure that these aspects are properly addressed. In this chapter we will discuss a number of ways of organizing the design and development process and various models that support specific design decisions during this process, concluding with a discussion of design patterns for serious games.
The presented research project will address parasocial interaction (PSI) directed towards non-player characters (NPCs) within video games. As first described by Horton and Wohl in 1956, the investigation of PSI has been predominantly limited to the context of linear media. Consequently, a significant research gap has emerged, prompting the need for this study. This research endeavors to bridge this gap by conducting multiple studies that delve into different aspects of a character's presence that seem to affect PSI. For example, factors such as obtrusiveness and persistence will be investigated due to their potential influence on the strength of PSI (Hartmann, Schramm, & Klimmt, 2004). Furthermore, the inquiry extends to exploring the collective impact of a group of NPCs on PSI dynamics. To achieve these objectives, the research will employ research through design methods, involving iterative modifications to the NPCs across various test setups. A game-based research environment will be created for participant exposure, leveraging the video game RimWorld (Ludeon Studios, 2018) as a foundational framework that can be adapted as necessary. Employing a quantitative approach, the studies will document the impact different aspects of a character’s presence have on the strength of PSI observed. The outcomes of this research endeavor will be disseminated among fellow game developers through artistic interventions, such as, for example, game jams. This approach seeks to not only contribute to the scholarly understanding of PSI but also offer practical insights in the context of game development.
The scientific challenge is about unraveling the secret of Brazilian and Dutch soccer by capturing successful elements of game play of both countries,, combining expertise from data science, computer science and sport science. Suggested features from literature, as well as several novel ones, will be considered and filtered on how they capture success in soccer. A manageable set of features will then be obtained from various available Dutch datasets (focusing on successful play). Subsequently, the same features will be used to compare playing styles between both countries. Features of game play will be approached from two different angles. The first angle (spearheaded by the Brazilian computer science partner) concerns features that capture the dynamics of game play and characterize aspects of formation on the pitch. The second angle (lead by the Dutch data science partner) will focus on how an attack is built up, and how key events (shots on goal, transitions from defenders to midfielders, etc.) can help to characterize this. For the comparison between countries data will be collected in four different age categories in Brazil and the Netherlands during official games, in order to compare (the development of) game play between both countries. Data will be collected by means of the Local Position Measurement System, for reasons of accuracy and consistency. The applied science part of this proposal is focusing on bridging the gap between fundamental science and soccer practice, i.e. coaches, trainers, clubs and federations. The outcomes of the fundamental part will be implemented in a coach-cockpit, a software application which trainers and coaches can use to (1) decide upon their strategy before a game, (2) analyze player- and team behaviour during a game enabling to adjust the strategy accordingly, and (3) choose and/or design training forms to improve player- and team behaviour.
The scientific challenge is about unraveling the secret of Brazilian and Dutch soccer by capturing successful elements of game play of both countries,, combining expertise from data science, computer science and sport science. Suggested features from literature, as well as several novel ones, will be considered and filtered on how they capture success in soccer. A manageable set of features will then be obtained from various available Dutch datasets (focusing on successful play). Subsequently, the same features will be used to compare playing styles between both countries. Features of game play will be approached from two different angles. The first angle (spearheaded by the Brazilian computer science partner) concerns features that capture the dynamics of game play and characterize aspects of formation on the pitch. The second angle (lead by the Dutch data science partner) will focus on how an attack is built up, and how key events (shots on goal, transitions from defenders to midfielders, etc.) can help to characterize this. For the comparison between countries data will be collected in four different age categories in Brazil and the Netherlands during official games, in order to compare (the development of) game play between both countries. Data will be collected by means of the Local Position Measurement System, for reasons of accuracy and consistency. The applied science part of this proposal is focusing on bridging the gap between fundamental science and soccer practice, i.e. coaches, trainers, clubs and federations. The outcomes of the fundamental part will be implemented in a coach-cockpit, a software application which trainers and coaches can use to (1) decide upon their strategy before a game, (2) analyze player- and team behaviour during a game enabling to adjust the strategy accordingly, and (3) choose and/or design training forms to improve player- and team behaviour.
