1,479 research outputs found
Partially Observable Games for Secure Autonomy
Technology development efforts in autonomy and cyber-defense have been evolving independently of each other, over the past decade. In this paper, we report our ongoing effort to integrate these two presently distinct areas into a single framework. To this end, we propose the two-player partially observable stochastic game formalism to capture both high-level autonomous mission planning under uncertainty and adversarial decision making subject to imperfect information. We show that synthesizing sub-optimal strategies for such games is possible under finite-memory assumptions for both the autonomous decision maker and the cyber-adversary. We then describe an experimental testbed to evaluate the efficacy of the proposed framework
Partially Observable Games for Secure Autonomy
Technology development efforts in autonomy and cyber-defense have been
evolving independently of each other, over the past decade. In this paper, we
report our ongoing effort to integrate these two presently distinct areas into
a single framework. To this end, we propose the two-player partially observable
stochastic game formalism to capture both high-level autonomous mission
planning under uncertainty and adversarial decision making subject to imperfect
information. We show that synthesizing sub-optimal strategies for such games is
possible under finite-memory assumptions for both the autonomous decision maker
and the cyber-adversary. We then describe an experimental testbed to evaluate
the efficacy of the proposed framework
Partially Observable Games for Secure Autonomy
Technology development efforts in autonomy and cyber-defense have been evolving independently of each other, over the past decade. In this paper, we report our ongoing effort to integrate these two presently distinct areas into a single framework. To this end, we propose the two-player partially observable stochastic game formalism to capture both high-level autonomous mission planning under uncertainty and adversarial decision making subject to imperfect information. We show that synthesizing sub-optimal strategies for such games is possible under finite-memory assumptions for both the autonomous decision maker and the cyber-adversary. We then describe an experimental testbed to evaluate the efficacy of the proposed framework
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