کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6874671 | 1441188 | 2018 | 20 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Adapting attackers and defenders patrolling strategies: A reinforcement learning approach for Stackelberg security games
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
This paper presents a novel approach for adapting attackers and defenders preferred patrolling strategies using reinforcement learning (RL) based-on average rewards in Stackelberg security games. We propose a framework that combines three different paradigms: prior knowledge, imitation and temporal-difference method. The overall RL architecture involves two highest components: the Adaptive Primary Learning architecture and the Actor-critic architecture. In this work we consider that defenders and attackers conforms coalitions in the Stackelberg security game, these are reached by computing the Strong Lp-Stackelberg/Nash equilibrium. We present a numerical example that validates the proposed RL approach measuring the benefits for security resource allocation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Computer and System Sciences - Volume 95, August 2018, Pages 35-54
Journal: Journal of Computer and System Sciences - Volume 95, August 2018, Pages 35-54
نویسندگان
Kristal K. Trejo, Julio B. Clempner, Alexander S. Poznyak,