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A hybrid method for effective management of the uncertainty in army decision making using cognitive agents and Classification based on Fuzzy Association Rules

机译:一种杂交方法,用于利用基于模糊关联规则的认知代理和分类的军队决策不确定性的有效管理

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Decision making is an important problem in most of the army operations. One of the challenging issues in this area is uncertainty in wars with uncertain information which causes many destructive effects on the results of strategies in battlefields. Cognitive Agent (CA) brings about an improvement by reducing the uncertainties meanwhile causes some unavoidable negative effects in critical decisions. Classification based on Fuzzy Association Rules (CFAR) is an effective method for rule mining which has the ability to deal with Sharp Boundary problems due to its flexibility in quantitative attribute domains and therefore it reduces the uncertainty. In this article a new hybrid method is proposed to reduce the uncertainty in decision making based on CA and CFAR. Simulation results show that the proposed method provides more understandable and has lower risk and more precise and flexibility as it reduces the generated rules.
机译:决策是大多数陆军运营中的重要问题。这一领域的一个具有挑战性的问题是具有不确定信息的战争的不确定性,这对战场上的策略结果产生了许多破坏性影响。认知剂(CA)通过降低不确定性,提高了改善,同时导致关键决策中的一些不可避免的负面影响。基于模糊关联规则(CFAR)的分类是规则挖掘的有效方法,其能够通过其在定量属性域中的灵活性来处理尖锐的边界问题,因此它降低了不确定性。在本文中,提出了一种新的混合方法,以减少基于CA和CFAR的决策的不确定性。仿真结果表明,该方法提供了更加理解的风险,风险较低,更精确,灵活性,因为它减少了生成的规则。

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