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UNDERSTANDING MILITARY INFORMATION PROCESSING - AN APPROACH TO SUPPORT INTELLIGENCE IN DEFENCE AND SECURITY

机译:了解军事信息处理 - 一种支持防御和安全智力的方法

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Decision makers in defence and security require timely and accurate understanding of the situation in their respective area of responsibility as well as a prediction of the likely intentions and capabilities of supposed or potential adversaries. To achieve this, intelligence cells have to process large volumes of information and data from all kinds of sources to deduce situation awareness. Particularly in the area of non-conventional conflicts, e.g. in the fight against terrorism, heterogeneous and complex non-military information factors are influencing the production of intelligence. To be able to support automatically the production of intelligence, a sound understanding of the principles of reasoning and the cognitive processes of the operators in the human dominated area of heuristic information processing and fusion has to be developed. The main information processing steps in intelligence are described and recommendations for automatic support are given. Semantically based flexible information structuring methods are a necessary precondition to provide the user with automated support for the exploitation and fusion of information from unstructured text. A second challenging aspect in the automation of information fusion is induced by the heuristic nature of the real human processing of imperfect information. The human method of default reasoning, based on knowledge about the behaviour or structure of adversary factions, can be used as an template based approach to support the Collation, Analysis and Integration of information in intelligence.
机译:防御和安全的决策者需要及时,准确地了解各自的责任领域的情况以及预测所谓或潜在的对手的可能意图和能力。为实现这一目标,智能细胞必须从各种来源处理大量的信息和数据,以推导出局势意识。特别是在非传统冲突领域,例如在打击恐怖主义的斗争中,异构和复杂的非军事信息因素正在影响智力的生产。为了能够自动支持智能的生产,必须开发对推理原则的理解,并且必须开发出启发式信息处理和融合的人体主导地区的运营商的认知过程。描述了智能的主要信息处理步骤,并给出了用于自动支持的建议。基于语义基于的灵活信息结构化方法是为用户提供自动支持的必要前提,用于从非结构化文本的信息的利用和融合。在信息融合自动化中的第二个具有挑战性的方面是由不完美信息的真实人体处理的启发式性质引起的。基于关于对抗派系的行为或结构的知识的基于基于模板的方法,可以用作基于模板的方法,以支持智能信息的整理,分析和整合。

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