Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
403079 | Knowledge-Based Systems | 2010 | 7 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Ronald Metoyer, Simone Stumpf, Christoph Neumann, Jonathan Dodge, Jill Cao, Aaron Schnabel,