智能因果关系的贝叶斯图
Bayesian Graphs of Intelligent Causation
AI总结：
本文将对抗风险分析与因果图模型结合，提出在对抗性环境中建模对手智能规避干预反应的贝叶斯图方法，并通过渗透场景验证其有效性。
摘要：
关于因果关系的概率图贝叶斯模型持续对战略分析产生影响，这些分析旨在帮助评估不同干预措施对系统的效能。然而，这些推断所依赖的标准因果代数通常假设被干预的群体不会做出智能反应来挫败干预。在对抗性环境中，这很少是一个合适的假设。在本文中，我们扩展了一种被称为对抗风险分析的既有贝叶斯方法，将其应用于在这种图意义上可以被合理地指定为因果的环境。为了嵌入这一技术，我们首先需要推广因果图的概念。随后，我们展示如何在这种图框架内系统地建模对手在得知干预后为规避干预而做出的可预测的智能反应，并引入贝叶斯博弈论中的这些最新进展。通过一个与对手试图渗透一个友好国家相关的应用，说明了这些新方法和配套协议。
英文摘要：
Probabilistic Graphical Bayesian models of causation have continued to impact on strategic analyses designed to help evaluate the efficacy of different interventions on systems. However, the standard causal algebras upon which these inferences are based typically assume that the intervened population does not react intelligently to frustrate an intervention. In an adversarial setting this is rarely an appropriate assumption. In this paper, we extend an established Bayesian methodology called Adversarial Risk Analysis to apply it to settings that can legitimately be designated as causal in this graphical sense. To embed this technology we first need to generalize the concept of a causal graph. We then proceed to demonstrate how the predicable intelligent reactions of adversaries to circumvent an intervention when they hear about it can be systematically modelled within such graphical frameworks, importing these recent developments from Bayesian game theory. The new methodologies and supporting protocols are illustrated through applications associated with an adversary attempting to infiltrate a friendly state.
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