In early game development phases game designers adjust game rules in a rapid, iterative and flexible way. In later phases, when software prototypes are available, play testing provides more detailed feedback about player experience. More often than not, the realized and the intended gameplay emerging from game software differ. Unfortunately, adjusting it is hard because designers lack a means for efficiently defining, fine-tuning and balancing game mechanics. The language Machinations provides a graphical notation for expressing the rules of game economies that fits with a designer's understanding and vocabulary, but is limited to design itself. Micro-Machinations (MM) formalizes the meaning of core language elements of Machinations enabling reasoning about alternative behaviors and assessing quality, making it also suitable for software development. We propose an approach for designing, embedding and adapting game mechanics iteratively in game software, and demonstrate how the game mechanics and the gameplay of a tower defense game can be easily changed and promptly play tested. The approach shows that MM enables the adaptability needed to reduce design iteration times, consequently increasing opportunities for quality improvements and reuse.
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Proper decision-making is one of the most important capabilities of an organization. Therefore, it is important to have a clear understanding and overview of the decisions an organization makes. A means to understanding and modeling decisions is the Decision Model and Notation (DMN) standard published by the Object Management Group in 2015. In this standard, it is possible to design and specify how a decision should be taken. However, DMN lacks elements to specify the actors that fulfil different roles in the decision-making process as well as not taking into account the autonomy of machines. In this paper, we re-address and-present our earlier work [1] that focuses on the construction of a framework that takes into account different roles in the decision-making process, and also includes the extent of the autonomy when machines are involved in the decision-making processes. Yet, we extended our previous research with more detailed discussion of the related literature, running cases, and results, which provides a grounded basis from which further research on the governance of (semi) automated decision-making can be conducted. The contributions of this paper are twofold; 1) a framework that combines both autonomy and separation of concerns aspects for decision-making in practice while 2) the proposed theory forms a grounded argument to enrich the current DMN standard.
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In the multi-billion dollar game industry, time to market limits the time developers have for improving games. Game designers and software engineers usually live on opposite sides of the fence, and both lose time when adjustments best understood by designers are implemented by engineers. Designers lack a common vocabulary for expressing gameplay, which hampers specification, communication and agreement. We aim to speed up the game development process by improving designer productivity and design quality. The language Machinations has introduced a graphical notation for expressing the rules of game economies that is close to a designer’s vocabulary. We present the language Micro- Machinations (MM) that details and formalizes the meaning of a significant subset of Machination’s language features and adds several new features most notably modularization. Next we describe MM Analysis in Rascal (MM AiR), a framework for analysis and simulation of MM models using the Rascal meta-programming language and the Spin model checker. Our approach shows that it is feasible to rapidly simulate game economies in early development stages and to separate concerns. Today’s meta-programming technology is a crucial enabler to achieve this.
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