Abstract
Real-time strategy games share many aspects with real situations in domains such as battle planning, air traffic control, and emergency response team management which makes them appealing test-beds for Artificial Intelligence (AI) and machine learning. End user annotations could help to provide supplemental information for learning algorithms, especially when training data is sparse. This paper presents a formative study to uncover how experienced users explain game play in real-time strategy games. We report the results of our analysis of explanations and discuss their characteristics that could support the design of systems for use by experienced real-time strategy game users in specifying or annotating strategy-oriented behavior.
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Metoyer, R., Stumpf, S., Neumann, C., Dodge, J., Cao, J., Schnabel, A. (2010). Explaining How to Play Real-Time Strategy Games. In: Bramer, M., Ellis, R., Petridis, M. (eds) Research and Development in Intelligent Systems XXVI. Springer, London. https://doi.org/10.1007/978-1-84882-983-1_18
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DOI: https://doi.org/10.1007/978-1-84882-983-1_18
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