Abstract
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 (real-time 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.
Highlights
Computer games have been linked with artificial intelligence (AI) since the first program was designed to play chess (Shannon 1950)
The selection focuses on Player Experience Modelling (PEM), Natural Language Processing (NLP), and advanced Non-Playing Character modelling (NPC), respectively, all of which are among the flagships of game AI research (Yannakakis 2012; Yannakakis and Togelius 2015)
This article presented a comprehensive overview of AI advances for serious games
Summary
Computer games have been linked with artificial intelligence (AI) since the first program was designed to play chess (Shannon 1950). Various authors (Champandard 2004; Bourassa and Massey 2012; Yannakakis 2012; Yannakakis and Togelius 2018) have pointed at the marginal penetration of academic game AI methods in industrial game production This limited uptake has been attributed to 1) research projects largely focusing on advanced, but non-scalable projects of little commercial or practical value, and 2) game studios reluctant to adopt and include promising but risky AI techniques (such as neural networks) rather than established, fully scripted technologies in their games. Research policy makers and politicians both at national and international levels have recognised that the transfer of knowledge and technologies from research and development organisations to societal sectors in order to create economic and social value, is a fundamental problem that should be urgently addressed. Key results from the various application pilots will be summarised and discussed in the light of the anticipated knowledge and technology transfer mechanism for the serious gaming community
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