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Successful and accurate modelling of level difficulty is a fundamental component of the operationalisation of player experience as difficulty is one of the most important and commonly used signals for content design and adaptation. In games that feature intermediate milestones, such as completable areas or levels, difficulty is often defined by the probability of completion or completion rate; however, this operationalisation is limited in that it does not describe the behaviour of the player within the area. In this research work, we formalise a model of level difficulty for puzzle games that goes beyond the classical probability of success. We accomplish this by describing the distribution of actions performed within a game level using a parametric statistical model thus creating a richer descriptor of difficulty. The model is fitted and evaluated on a dataset collected from the game Lily's Garden by Tactile Games, and the results of the evaluation show that the it is able to describe and explain difficulty in a vast majority of the levels.

With advances in Artificial Intelligence and Machine Learning, traditional SCADA and manual statistical modelling are likely to be replaced. Advanced Statistical Modelling based on offline data is resource intensive and ultimately cannot provide real-time analytics that are actionable. As Automated Machine Learning gains traction, we expect more industrial plants to rely on this solution for Predictive Maintenance.

What is the difference between Machine Learning and Statistical modeling? I have been doing research for the past 2 years. Generally, it takes me not more than a day to get clear answer to the topic I am researching for. However, this was definitely one of the harder nuts to crack. When I came across this question at first, I found almost no clear answer which can layout how machine learning is different from statistical modeling.