Turn-based strategy games and simulations are vital tools for military education, training, and readiness. In an era of increasingly constrained resources and expanding demand for training solutions, the need for validated, effective solutions will increase. Appropriate performance feedback is an important component of any training solution. Current methods for designing and testing the performance feedback provided in turn-based simulation are limited to well-structured problems and do not adequately address ill-structured problems that better replicate problems facing military leaders in todays complex operating environment. This thesis develops and explores new methods for assessing the feedback mechanisms of turn-based strategy games. Using UrbanSim, a game for training strategic approaches to COIN operations as an exemplar, this thesis developed and explored two unique methods for evaluating the reward structure of the UrbanSim scenarios. The first method evaluates different student strategies using a batch-run method. The second method uses a reinforcement-learning algorithm to explore the decision space. These scenario evaluation methodologies are shown to be able to provide insights about a games performance feedback mechanism that was not previously available. These methodologies can be used for formative evaluation during game scenario development. Additionally, these evaluation methodologies are generalizable to other training and education games that focus on ill-structured problems and decision-making at discrete intervals.

Approved for public release; distribution is unlimited.

http://archive.org/details/amethodologytoas1094517472

This thesis was performed at the MOVES Institute

Major, United States Army