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A Self-adapting Heuristic for Automatically Constructing TerrainAppreciation Exercises

机译:自适应启发式算法,可自动构建地形赏析练习

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Appreciating terrain is a key to success in both symmetric and asymmetric forms of warfare. Training to enable Soldiers to master this vital skill has traditionally required their translocation to a selected number of areas, each affording a desired set of topographical features, albeit with limited breadth of variety. As a result, the use of such methods has proved to be costly and time consuming. To counter this, new computer-aided training applications permit users to rapidly generate and complete training exercises in geo-specific open and urban environments rendered by high-fidelity image generation engines. The latter method is not only cost-efficient, but allows any given exercise and its conditions to be duplicated or systematically varied over time. However, even such computer-aided applications have shortcomings. One of the principal ones is that they usually require all training exercises to be painstakingly constructed by a subject matter expert. Furthermore, exercise difficulty is usually subjectively assessed and frequently ignored thereafter. As a result, such applications lack the ability to grow and adapt to the skill level and learning curve of each trainee. In this paper, we present a heuristic that automatically constructs exercises for identifying key terrain. Each exercise is created and administered in a unique iteration, with its level of difficulty tailored to the trainee's ability based on the correctness of that trainee's responses in prior iterations.
机译:在对称和非对称战争中,领略地形是成功的关键。传统上,为了使士兵们掌握这项至关重要的技能而进行的培训需要将他们转移到一定数量的区域,每个区域都提供所需的地形特征集,尽管种类有限。结果,已经证明使用这种方法是昂贵且费时的。为了解决这个问题,新的计算机辅助培训应用程序允许用户在由高保真图像生成引擎渲染的特定于地理环境的开放和城市环境中快速生成并完成培训练习。后一种方法不仅具有成本效益,而且允许任何给定的练习及其条件随时间重复或系统地变化。但是,即使这样的计算机辅助应用程序也具有缺点。主要原则之一是,它们通常要求所有培训练习都由主题专家精心完成。此外,运动困难通常是主观评估,此后经常被忽略。结果,这样的应用缺乏发展和适应每个学员的技能水平和学习曲线的能力。在本文中,我们提出一种启发式方法,该方法可自动构建用于识别关键地形的练习。每次练习都是在一个唯一的迭代中创建和管理的,其难度级别根据学员在先前迭代中的响应的正确性而针对学员的能力进行定制。

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