Game theory is a tool for modeling multi-agent decision problems and has been used successfully in problems such as poker, security, and trading agents. However, many real games are extremely large[4]. One approach for solving large games is to use abstraction techniques to shrink the game to a form that can be solved by removing detail and translating a solution back to the original. However, abstraction introduces error into the model. We study ways to analyze games that are robust to errors in the model of the game, including abstracted games. We empirically evaluate several solution methods to evaluate how robust they are for abstracted games.
['Oscar Veliz', 'Christopher Kiekintveld']
Selecting Robust Strategies Based on Abstracted Game Models