An effective, efficient, and ecological transport service needs a decision support system to inform the administrations, while intelligently assisting citizens' daily transportation choices. This work presents a full-fledged Gamification Platform to develop and run sustainable mobility campaigns promoting individual green mobility behaviors. We organize geolocalized and temporal information on users' tracked trips into a rich dataset that decision-makers can exploit to analyze citizens' daily transportation choices and balance the demand and the offer of the mobility ecosystem. Through the Gamification Platform, we define three different use case scenarios - i.e., mobility within the city, home-work mobility, and home-school mobility. We also evaluate the platform according to both gameful design guidelines and persuasive technology best practices. Our findings show how long-term retention and engagement can be achieved when the gameful system's design and maintenance are assisted by the Gamification Platform, merging both administrative requirements and users' needs towards an iteratively adaptive and personalized experience.

When designing a serious game it is paramount to take into account that the players' performance may greatly vary. In many types of games the performance will impact the game experience, as it is related to a point acquisition mechanic. In specific contexts, such as multi-player non-competitive or collaborative games, the designer may desire to minimize this variability, reducing the difference in game progression between low-performing and high-performing players. In this paper we detail such a context: an educational serious game for sustainability addressed to classes of a primary school, where the low performance of a class may not be necessarily related to the children's effort. We thus present and evaluate a novel approach for the calibration of the game dynamics for achieving a more even player experience.

How can we run graphical inference on large graphs efficiently and accurately? Many real-world networks are modeled as graphical models, and graphical inference is fundamental to understand the properties of those networks. In this work, we propose a novel approach for fast and accurate inference, which first samples a small subgraph and then runs inference over the subgraph instead of the given graph. This is done by the bounded treewidth (BTW) sampling, our novel algorithm that generates a subgraph with guaranteed bounded treewidth while retaining as many edges as possible. We first analyze the properties of BTW theoretically. Then, we evaluate our approach on node classification and compare it with the baseline which is to run loopy belief propagation (LBP) on the original graph. Our approach can be coupled with various inference algorithms: it shows higher accuracy up to 13.7% with the junction tree algorithm, and allows faster inference up to 23.8 times with LBP. We further compare BTW with previous graph sampling algorithms and show that it gives the best accuracy.

We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian network greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. Our novel algorithm accomplishes this task, scaling both to large domains and to large treewidths. Our novel approach consistently outperforms the state of the art on experiments with up to thousands of variables.

We present a method for learning Bayesian networks from data sets containingthousands of variables without the need for structure constraints. Our approachis made of two parts. The first is a novel algorithm that effectively explores thespace of possible parent sets of a node. It guides the exploration towards themost promising parent sets on the basis of an approximated score function thatis computed in constant time. The second part is an improvement of an existingordering-based algorithm for structure optimization. The new algorithm provablyachieves a higher score compared to its original formulation. On very large datasets containing up to ten thousand nodes, our novel approach consistently outper-forms the state of the art.