Using the Crowd to Generate Content for Scenario-Based Serious-Games
This addresses the problem of content creation overhead for developers of serious-games in domains like law enforcement training, though it appears incremental as it builds on existing crowd-sourcing and planning methods.
The paper tackles the high overhead of manually creating content for scenario-based serious-games by proposing an automatic method that combines computer science techniques with crowd-sourcing to generate scenarios for medical, military, commerce, and gaming applications. The result shows that the generated scenarios are rated as reliable and consistent by the crowd, equally effective as traditional planning techniques, but with more varied content and easier creation.
In the last decade, scenario-based serious-games have become a main tool for learning new skills and capabilities. An important factor in the development of such systems is the overhead in time, cost and human resources to manually create the content for these scenarios. We focus on how to create content for scenarios in medical, military, commerce and gaming applications where maintaining the integrity and coherence of the content is integral for the system's success. To do so, we present an automatic method for generating content about everyday activities through combining computer science techniques with the crowd. We use the crowd in three basic ways: to capture a database of scenarios of everyday activities, to generate a database of likely replacements for specific events within that scenario, and to evaluate the resulting scenarios. We found that the generated scenarios were rated as reliable and consistent by the crowd when compared to the scenarios that were originally captured. We also compared the generated scenarios to those created by traditional planning techniques. We found that both methods were equally effective in generated reliable and consistent scenarios, yet the main advantages of our approach is that the content we generate is more varied and much easier to create. We have begun integrating this approach within a scenario-based training application for novice investigators within the law enforcement departments to improve their questioning skills.