Evolution of Strategies in Sequential Security Games
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In this paper we introduce a generic approach to solving Sequential Security Games (SGs) based on Evolutionary Algorithms. The method (named EASG) is general and largely game-independent, which allows for its application to a wide range of SGs with only slight adjustments. The efficacy of EASG is verified on 3 types of games from different domains: patrolling, surveillance, and cybersecurity. Comprehensive experiments performed on more than 300 test games demonstrate robustness and stability of EASG, manifested by repetitively achieving optimal or near-optimal solutions. The main advantage of EASG compared to alternative approaches to solving SGs is its time efficiency. EASG finds high-quality solutions with approximately linear time scalability and can therefore be applied to solving SG instances which are beyond capabilities of the state-of-the-art exact methods. Due to anytime characteristics, EASG is particularly well suited for time-critical applications.
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