Explaining how to play real-time strategy games

By Ronald A. Metoyer, Simone Stumpf, Christoph Neumann, Jonathan Dodge, Jill Cao, Aaron Schnabel. 2010

9 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Developmental and Educational Psychology

Citations in the corpus

• Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “…1994; Kim, Chacha, and Shah, 2013; Lane, Core, Van Lent, Solomon, and Gomboc, 2005; Langley, Meadows, Sridharan, and Choi, 2017; Lomas et al., 2012; Metoyer et al., 2010; Mnih et al., 2015; Moulin et al., 2002; Rosenthal, Selvaraj, and Veloso, 2016; S. Singh, Lewis, Barto, and Sorg, 2010; Stubbs,…”