For seizing the potential of serious games, the RAGE project—funded by the Horizon-2020 Programme of the European Commission—will make available an interoperable set of advanced technology components (software assets) that support game studios at serious game development. This paper describes the overall software architecture and design conditions that are needed for the easy integration and reuse of such software assets in existing game platforms. Based on the component-based software engineering paradigm the RAGE architecture takes into account the portability of assets to different operating systems, different programming languages, and different game engines. It avoids dependencies on external software frameworks and minimises code that may hinder integration with game engine code. Furthermore it relies on a limited set of standard software patterns and well-established coding practices. The RAGE architecture has been successfully validated by implementing and testing basic software assets in four major programming languages (C#, C++, Java, and TypeScript/JavaScript, resp.). Demonstrator implementation of asset integration with an existing game engine was created and validated. The presented RAGE architecture paves the way for large scale development and application of cross-engine reusable software assets for enhancing the quality and diversity of serious gaming.

Question Answering (QA), the process of computing valid answers to questions formulated in natural language, has recently gained attention in both industry and academia. Translating this idea to the realm of geographic information systems (GIS) may open new opportunities for data scientists. In theory, analysts may simply ask spatial questions to exploit diverse geographic information resources, without a need to know how GIS tools and geodata sets interoperate. In this outlook article, we investigate the scientific challenges of geo-analytical question answering, introducing the problems of unknown answers and indirect QA. Furthermore, we argue why core concepts of spatial information play an important role in addressing this challenge, enabling us to describe analytic potentials, and to compose spatial questions and workflows for generating answers. ARTICLE HISTORY

Complex problem solving is often an integration of perceptual processing and deliberate planning. But what balances these two processes, and how do novices differ from experts? We investigate the interplay between these two in the game of SET. This article investigates how people combine bottom-up visual processes and top-down planning to succeed in this game. Using combinatorial and mixed-effect regression analysis of eye-movement protocols and a cognitive model of a human player, we show that SET players deploy both bottom-up and top-down processes in parallel to accomplish the same task. The combination of competition and cooperation of both types of processes is a major factor of success in the game. Finally, we explore strategies players use during the game. Our findings suggest that within-trial strategy shifts can occur without the need of explicit meta-cognitive control, but rather implicitly as a result of evolving memory activations.

Loose programming enables analysts to program with concepts instead of procedural code. Data transformations are left underspecified, leaving out procedural details and exploiting knowledge about the applicability of functions to data types. To synthesize workflows of high quality for a geo‐analytical task, the semantic type system needs to reflect knowledge of geographic information systems (GIS) at a level that is deep enough to capture geo‐analytical concepts and intentions, yet shallow enough to generalize over GIS implementations. Recently, core concepts of spatial information and related geo‐analytical concepts were proposed as a way to add the required abstraction level to current geodata models. The core concept data types (CCD) ontology is a semantic type system that can be used to constrain GIS functions for workflow synthesis. However, to date, it is unknown what gain in precision and workflow quality can be expected. In this article we synthesize workflows by annotating GIS tools with these types, specifying a range of common analytical tasks taken from an urban livability scenario. We measure the quality of automatically synthesized workflows against a benchmark generated from common data types. Results show that CCD concepts significantly improve the precision of workflow synthesis.

This article provides a comprehensive overview of artificial intelligence (AI) for serious games. Reporting about the work of a European flagship project on serious game technologies, it presents a set of advanced game AI components that enable pedagogical affordances and that can be easily reused across a wide diversity of game engines and game platforms. Serious game AI functionalities include player modelling (realtime facial emotion recognition, automated difficulty adaptation, stealth assessment), natural language processing (sentiment analysis and essay scoring on free texts), and believable non-playing characters (emotional and socio-cultural, non-verbal bodily motion, and lip-synchronised speech), respectively. The reuse of these components enables game developers to develop high quality serious games at reduced costs and in shorter periods of time. All these components are open source software and can be freely downloaded from the newly launched portal at gamecomponents.eu. The components come with detailed installation manuals and tutorial videos. All components have been applied and validated in serious games that were tested with real end-users.