Abstract
Game theory is a tool for modeling multi-agent decision problems and has been used to great success in modeling and simulating problems such as poker, security, and trading agents. However, many real games are extremely large and complex with multiple agent interactions. One approach for solving these 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 examine how robust they are for abstracted games.
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This material is based upon work supported by the National Science Foundation under Grant No. IIS-1253950.
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Veliz, O., Kiekintveld, C. (2017). Selecting Robust Strategies Based on Abstracted Game Models. In: Bai, Q., Ren, F., Fujita, K., Zhang, M., Ito, T. (eds) Multi-agent and Complex Systems. Studies in Computational Intelligence, vol 670. Springer, Singapore. https://doi.org/10.1007/978-981-10-2564-8_6
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DOI: https://doi.org/10.1007/978-981-10-2564-8_6
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