Using HMM in Strategic Games

In this paper we describe an approach to resolve strategic games in which players can assume different types along the game. Our goal is to infer which type the opponent is adopting at each moment so that we can increase the player's odds. To achieve that we use Markov games combined with hidden Markov model. We discuss a hypothetical example of a tennis game whose solution can be applied to any game with similar characteristics.Comment: In Proceedings DCM 2013, arXiv:1403.768

Dominion: A Game of Information Exploitation

FlipIt is an abstract cyber-security game published in 2012 to investigate optimal strategies for managing security resources in response to Advanced Persistent Threats. In this thesis, we place FlipIt within a more general category of \u27stealthy move\u27 games, and provide an approach towards solving such games. We produce a new stealthy move game, \u27Dominion\u27, and derive Nash equilibria for it. We establish bounds for the optimal rates of play and benefits for FlipIt, and show that the best strategy to apply to real cyber security threats includes presenting a credible threat to potential players. We also explore the effects of initial game information asymmetry in Dominion