To select optimal routes for vehicles endurance tests, it is necessary to have a road map characterized by events of interest. In this context, we define events as effects on the vehicle triggered by some proprieties of the routes. Such a characterization strongly relies on data from previous test drives. If new road maps are to be considered, e.g., in a different region, the route selection rather depends on the experience of engineers, which can lead to suboptimal decisions. To overcome this problem, we propose using the existing data from prior test drives to train a machine learning (ML) model, which then transfers this knowledge to unseen road maps. To this end, we formulate a sequential problem that can be solved with state-of-the-art ML architectures. Our experimental results based on real-world data show the potential of the proposed approach as we illustrate for the case of testing energy recuperation in electric vehicles.

In recent years, microblogging platforms have not only become an important communication channel for the game industry to generate and uphold audience interest but also a rich resource for gauging player opinion. In this paper we use data gathered from Twitter to examine which topics matter to players and to identify influential members of a game's community. By triangulating in-game data with Twitter activity we explore how tweets can provide contextual information for understanding fluctuations in in-game activity. To facilitate analysis of the data we introduce a visual data exploration tool and use it to analyze tweets related to the game Destiny. In total, we collected over one million tweets from about 250,000 users over a 14-month period and gameplay data from roughly 3,500 players over a six-month period.

We present a new approach to enhancing answer set programming (ASP) with constraint programming (CP) techniques based on conflict-driven learning and lazy nogood generation.

We solve constraint satisfaction problems through translation to answer set programming (ASP). Our reformulations have the property that unitpropagation in the ASP solver achieves well defined local consistency properties like arc, bound and range consistency. Experiments demonstrate the computational value of this approach. 1

Abstract We present a new approach to enhancing Answer Set Programming (ASP) with Constraint Processing techniques which allows for solving interesting Constraint Satisfaction Problems in ASP. We show how constraints on finite domains can be decomposed into logic programs such that unit-propagation achieves arc, bound or range consistency. Experiments with our encodings demonstrate their computational impact.