Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Catcher-Evader Games / 329
Yuqian Li, Vincent Conitzer, Dmytro Korzhyk

Algorithms for computing game-theoretic solutions have recently been applied to a number of security domains. However, many of the techniques developed for compact representations of security games do not extend to Bayesian security games, which allow us to model uncertainty about the attacker's type. In this paper, we introduce a general framework of catcher-evader games that can capture Bayesian security games as well as other game families of interest. We show that computing Stackelberg strategies is NP-hard, but give an algorithm for computing a Nash equilibrium that performs well in experiments. We also prove that the Nash equilibria of these games satisfy the interchangeability property, so that equilibrium selection is not an issue.