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Bayesian Stackelberg Game Model for Water Supply Networks against Bounded Rational Interdictors

机译:贝叶斯·斯塔尔伯格供水网络博弈模型对有界利特合理算子的供水网络

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This work addresses how to optimize defense strategies for water supply networks against bounded rational interdictors (BRIs). BRIs may take either rational interdictions (RIs) or irrational interdictions (IIs), where RIs are strategic behavior oriented by the goal of maximizing the damage to network structure and minimizing interdiction costs, and IIs are non-strategic behavior, such as random attacks. Thus, we develop a Bayesian Stackelberg game model, where the defender initiates the game, with the objective of minimizing defense cost and minimizing the impacts on water satisfaction rate caused by either RIs or IIs, and then BRIs follow the game. In this model, since interdictions are probabilistic to be successful, BRIs hold different risk attitude to take such interdictions, which is assumed to be BRIs' private information. Besides, the probability of each component to be irrationally interdicted by BRIs is characterized based on a learning model. Then, we propose a model-based genetic algorithm to obtain the equilibriums of the Bayesian Stackelberg game model. The case study of D-Town water network demonstrates the practical applicability and discuss how to defend water supply networks against BRIs.
机译:这项工作涉及如何针对有限理性拦截舰(BRIS)供水管网优化的防御策略。变的Bris可采取任一合理的遮断(RIS)或不合理的遮断器(IIS),其中的RI是通过最大化到网络结构的损伤和减少遮断成本的目标定向战略行为,和IIS非战略行为,诸如随机攻击。因此,我们开发了一个斯坦博格贝叶斯博弈模型,其中后卫发起的比赛中,与客观减少国防成本和减少而导致的任何注册机构或非法入境者在水中满意率的影响,然后变的Bris遵守游戏。在这个模型中,由于遮断的概率是成功的,变的Bris持有不同风险的态度采取这样的阻截,这被认为是变的Bris的私人信息。此外,各成分的概率被不合理通过BRIS停职基于学习模型的特征在于。然后,我们提出了一种基于模型的遗传算法来获得施塔贝格贝叶斯博弈模型的平衡点。 d镇水网的案例研究表明实用性,讨论如何抵御变的Bris供水管网。

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