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  1. Free, publicly-accessible full text available December 23, 2024
  2. Fu, J. (Ed.)
    Free, publicly-accessible full text available December 23, 2024
  3. This paper studies language-based opacity enforcement in a two-player, zero-sum game on a graph. In this game, player 1 (P1) wins if he can achieve a secret temporal goal described by the language of a finite automaton, no matter what strategy the opponent player 2 (P2) selects. In addition, P1 aims to win while making its goal opaque to a passive observer with imperfect information. However, P2 colludes with the observer to reveal P1's secret whenever P2 cannot prevent P1 from achieving its goal, and therefore, opacity must be enforced against P2. We show that a winning and opacity-enforcing strategy for P1 can be computed by reducing the problem to solving a reachability game augmented with the observer's belief states. Furthermore, if such a strategy does not exist, winning for P1 must entail the price of revealing his secret to the observer. We demonstrate our game-theoretic solution of opacity-enforcement control through a small illustrative example and in a robot motion planning problem. 
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  4. This letter focuses on the optimal allocation of multi-stage attacks with the uncertainty in attacker’s intention. We model the attack planning problem using a Markov decision process and characterize the uncertainty in the attacker’s intention using a finite set of reward functions—each reward represents a type of attacker. Based on this modeling, we employ the paradigm of the worst-case absolute regret minimization from robust game theory and develop mixed-integer linear program (MILP) formulations for solving the worst-case regret minimizing sensor allocation strategies for two classes of attack-defend interactions: one where the defender and attacker engage in a zero-sum game and another where they engage in a non-zero-sum game. We demonstrate the effectiveness of our algorithm using a stochastic gridworld example. 
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