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Abductive Inference for Combat: Using SCARE-S2 to Find High-Value Targets in Afghanistan

机译:对抗的绑架推断:使用Scare-S2在阿富汗寻找高价值目标

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Recently, geospatial abduction was introduced by the authors in (Shakarian, Subrahmanian, and Sapino 2010) as a way to infer unobserved geographic phenomena from a set of known observations and constraints between the two. In this paper, we introduce the SCARES2 software tool which applies geospatial abduction to the environment of Afghanistan. Unlike previous work, where we looked for small weapon caches supporting local attacks, here we look for insurgent high-value targets (HVT's), supporting insurgent operations in two provinces. These HVT's include the locations of insurgent leaders and major supply depots. Applying this method of inference to Afghanistan introduces several practical issues not addressed in previous work. Namely, we are conducting inference in a much larger area (24,940 sq km as compared to 675 sq km in previous work), on more varied terrain, and must consider the influence of many local tribes. We address all of these problems and evaluate our software on 6 months of real-world counter-insurgency data. We show that we are able to abduce regions of a relatively small area (on average, under 100 sq km and each containing, on average, 4.8 villages) that are more dense with HVT's (35 × more than the overall area considered).
机译:最近,作者在(Shakaria,Subrahmanian和Sapino 2010)中引入了地理空间绑架,作为从两组已知观察和约束之间推断出未观察到的地理现象的方式。在本文中,我们介绍了对阿富汗环境应用地理空间绑架的Scares2软件工具。与以往的工作不同,我们寻找支持当地攻击的小型武器缓存,我们在这里寻找叛乱高价值目标(HVT),支持两省的叛乱行动。这些HVT包括叛乱领导人和主要供应仓库的地点。将这种推理方法应用于阿富汗,在以前的工作中引入了几个未解决的实际问题。即,我们在更大的区域(24,940平方公里,与以前的工作中的675平方公里相比)进行推理,在更具不同的地形上,并且必须考虑许多当地部落的影响。我们解决了所有这些问题,并在现实世界反叛乱数据的6个月内评估我们的软件。我们表明,我们能够诱使相对较小的区域(平均而不是100平方公里,每个村庄,平均,4.8个村庄)的区域,这些内容与HVT更密集(35倍超过总体区域)。

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