On the reasoning patterns of agents in games
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Using reasoning patterns to simplify games
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Using reasoning patterns to help humans solve complex games
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Representing Bayesian games without a common prior
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Human factors in computer decision-making
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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Bayesian games have been traditionally employed to describe and analyze situations in which players have private information or are uncertain about the game being played. However, computing Bayes-Nash equilibria can be costly, and becomes even more so if the common prior assumption (CPA) has to be abandoned, which is sometimes necessary for a faithful representation of real-world systems. We propose using the theory of reasoning patterns in Bayesian games to circumvent some of these difficulties. The theory has been used successfully in common knowledge (non-Bayesian) games, both to reduce the computational cost of finding an equilibrium and to aid human decision-makers in complex decisions. In this paper, we first show that reasoning patterns exist for every decision of every Bayesian game, in which the acting agent has a reason to deliberate. This implies that reasoning patterns are a complete characterization of the types of reasons an agent might have for making a decision. Second, we illustrate practical applications of reasoning patterns in Bayesian games, which allow us to answer questions that would otherwise not be easy in traditional analyses, or would be extremely costly. We thus show that the reasoning patterns can be a useful framework in analyzing complex social interactions.