Advances in domains such as sensor networks and electronic and ambient intelligence have allowed us to create intelligent environments (IEs). However, research in IE is being held back by the fact that researchers face major difficulties, such as a lack of resources for their experiments. Indeed, they cannot easily build IEs to evaluate their approaches. This is mainly because of economic and logistical issues. In this paper, we propose a simulator to build virtual IEs. Simulators are a good alternative to physical IEs because they are inexpensive, and experiments can be conducted easily. Our simulator is open source and it provides users with a set of virtual sensors that simulates the behavior of real sensors. This simulator gives the user the capacity to build their own environment, providing a model to edit inhabitants’ behavior and an interactive mode. In this mode, the user can directly act upon IE objects. This simulator gathers data generated by the interactions in order to produce datasets. These datasets can be used by scientists to evaluate several approaches in IEs.

Difficulty constitutes a key component of games. It both serves as motivation to play the game and as a way to control progression. Usually, a video game offers a gradual progression in gameplay difficulty, starting with easy levels that allow players to grasp the basic mechanics and incrementally introducing more challenging obstacles as they progress. Needless to say, the success of a game heavily relies on its ability to provide a well-balanced difficulty curve and a satisfying progression. Therefore, designing a game entails a complex and time-consuming process that involves extensive playtesting. One potential approach to address this challenge is the utilization of software tools capable of automatically evaluating the difficulty of game levels. In this article, we present a comprehensive model for automatically evaluating the difficulty levels of platformer games. Our model is based on the formal calculation of static danger zones within levels and the analysis of enemy movement patterns using simulated pheromones. To validate our model, we implemented it and conducted tests using the complete set of levels from the game Super Mario Bros. Futhermore, the article includes a detailed presentation of the model, the tools developed, a comparative analysis showing the computed results, and a discussion on the limitations and advantages.