Adapting Game Mechanics with Micro-Machinations

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-Mach

Debugging procedural level designs with mental maps

Procedural Level Generation provides tools and techniques for generating many game levels from a single specification. Instead of creating levels by hand, level designers make use of generators that automate the creation process. However, iteratively improving a level's design requires encoding generators of adventures, puzzles and encounters in notations that bear little resemblance to generated content. Raising the level quality is difficult, because it is hard to reason about bugs that can manifest inside generated content. We take the position that debugging requires special attention. We argue that advancing the area of PCG calls for tools and debugging techniques that speed up procedural level design and empower level designers. We propose exploring how Domain-Specific Languages can help in authoring a level’s design, validating the generator’s code, and debugging issues in generated content. We introduce Mental Maps, a visual language that expresses the spacial relations between rooms, objects and paths. We discuss how Mental Maps can serve as generator blueprints before the generation happens, and as debugging lenses for projecting issues afterwards

Does atrial fibrillation affect prognosis in hospitalised COVID-19 patients? A multicentre historical cohort study in the Netherlands

Objectives The aim of this multicentre COVID-PREDICT study (a nationwide observational cohort study that aims to better understand clinical course of COVID-19 and to predict which COVID-19 patients should receive which treatment and which type of care) was to determine the association between atrial fibrillation (AF) and mortality, intensive care unit (ICU) admission, complications and discharge destination in hospitalised COVID-19 patients.Setting Data from a historical cohort study in eight hospitals (both academic and non-academic) in the Netherlands between January 2020 and July 2021 were used in this study.Participants 3064 hospitalised COVID-19 patients >18 years old.Primary and secondary outcome measures The primary outcome was the incidence of new-onset AF during hospitalisation. Secondary outcomes were the association between new-onset AF (vs prevalent or non-AF) and mortality, ICU admissions, complications and discharge destination, performed by univariable and multivariable logistic regression analyses.Results Of the 3064 included patients (60.6% men, median age: 65 years, IQR 55–75 years), 72 (2.3%) patients had prevalent AF and 164 (5.4%) patients developed new-onset AF during hospitalisation. Compared with patients without AF, patients with new-onset AF had a higher incidence of death (adjusted OR (aOR) 1.71, 95% CI 1.17 to 2.59) an ICU admission (aOR 5.45, 95% CI 3.90 to 7.61). Mortality was non-significantly different between patients with prevalent AF and those with new-onset AF (aOR 0.97, 95% CI 0.53 to 1.76). However, new-onset AF was associated with a higher incidence of ICU admission and complications compared with prevalent AF (OR 6.34, 95% CI 2.95 to 13.63, OR 3.04, 95% CI 1.67 to 5.55, respectively).Conclusion New-onset AF was associated with an increased incidence of death, ICU admission, complications and a lower chance to be discharged home. These effects were far less pronounced in patients with prevalent AF. Therefore, new-onset AF seems to represent a marker of disease severity, rather than a cause of adverse outcomes