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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-支持国防和安全情报的方法

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摘要

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:国防和安全领域的决策者需要及时,准确地了解其各自职责范围内的情况,并需要对假定或潜在对手的可能意图和能力进行预测。为此,情报部门必须处理来自各种来源的大量信息和数据,以推断出态势感知能力。特别是在非常规冲突领域,例如在反恐斗争中,异构和复杂的非军事情报因素正在影响情报的产生。为了能够自动支持情报的产生,必须发展对人类主导的启发式信息处理和融合领域的推理原理和操作员认知过程的透彻理解。描述了智能中的主要信息处理步骤,并给出了自动支持的建议。基于语义的灵活信息结构化方法是为用户提供自动支持以利用和融合来自非结构化文本的信息的必要先决条件。信息融合自动化中的第二个挑战性方面是由不完善信息的真实人类处理的启发式性质引起的。基于关于敌对派系的行为或结构的知识的人为默认推理方法可以用作基于模板的方法,以支持情报中信息的整理,分析和集成。

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