Goal Recognition Design with Non-Observable Actions
Goal Recognition Design with Non-Observable Actions
复制标题
具有不可观察动作的目标识别设计
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
E. Karpas
中科院分区：
文献类型：
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作者：
Sarah Keren;A. Gal;E. Karpas
目标识别设计涉及目标识别模型的离线分析，通过制定评估模型内执行目标识别能力的措施，并找到有效的方法来计算和优化它们。在本文中，我们通过提供一个新的具有不可观察动作的目标识别设计的广义模型，放宽了先前工作的完全可观察性假设。具有部分可观察性的模型与辅助认知和安全等目标识别应用相关，这些应用由于传感器故障或缺乏足够的预算而导致可观察性降低。特别是，我们定义了一个最坏情况显著性（wcd）度量，它表示智能体在其轨迹的观察部分揭示其目标之前可以在系统中采取的最大步数。我们提出了一种基于经典规划的新编译计算wcd的方法，并提出了一种利用传感器放置来改进设计的方法。我们的实证评估表明，所提出的解决方案有效地计算和改进了wcd。
Goal recognition design involves the offline analysis of goal recognition models by formulating measures that assess the ability to perform goal recognition within a model and finding efficient ways to compute and optimize them. In this work we relax the full observability assumption of earlier work by offering a new generalized model for goal recognition design with non-observable actions. A model with partial observability is relevant to goal recognition applications such as assisted cognition and security, which suffer from reduced observability due to sensor malfunction or lack of sufficient budget. In particular we define a worst case distinctiveness (wcd) measure that represents the maximal number of steps an agent can take in a system before the observed portion of his trajectory reveals his objective. We present a method for calculating wcd based on a novel compilation to classical planning and propose a method to improve the design using sensor placement. Our empirical evaluation shows that the proposed solutions effectively compute and improve wcd.