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An agent-based approach to decluttering the interfaces of multi-UAV command and control systems

机译：基于代理的方法来整理多UAV命令和控制系统的接口

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Information overload and cluttered user interfaces can lead to decreased situational awareness and lowered performance of human operators. Irrelevant data increases searching times for tasks requiring the identification of threats, causing delayed decisions. Cognitive burden on the user increases as displays become more cluttered, which results in increased operator stress leading to poor decision-making ability. To address this issue, we have designed an intelligent agentbased system for the automatic de-cluttering of a representative net-centric interface designed for controlling multiple unmanned aerial vehicles (UAVs) by a single operator. Our concept is called ARID, for Agent-based Reduction of Information Density. The ARID hypothesis is that intelligent agents can improve operator performance by deemphasizing those aspects of a display that can be inferred as less-important to the mission goals. ARID agents receive information about the world via data feeds provided by various net-centric sources. Each agent has an understanding of the user interface symbols that are used to represent various entities, terrain features, and zones. The agent also is provided with a mission goal which is used for inferring the relevance of a given symbol to the success of the mission goal. First level facts, such as spatial relationships, are calculated by supporting agents and assigned a BDU (belief/disbelief/uncertainty) value. A dynamic set of rules provides an inference mechanism by which an agent can infer new facts from the given assertions. We have developed a Subjective Logic-based Evidential Reasoning Network that explicitly deals with belief and uncertainty in the knowledge base, and is used to derive a relevancy belief for every UI symbol in the map display. Subjective Logic is used to combine values when different sources provide different results for the same symbol. User Interface agents apply the results of the relevancy beliefs and transform the display to minimize the apparent clutter caused by less relevant elements. Two transformations, transparency and grouping, are used in the current implementation.
机译：信息过载和杂乱的用户界面可能导致人类运营商的情境感知和降低性能降低。无关的数据增加了需要识别威胁的任务的搜索时间，导致延迟决策。用户对用户的认知负担随着显示器变得更加杂乱而增加，这导致往来增加了导致决策能力较差的操作压力。为了解决这个问题，我们设计了一种智能代理系统，可实现用于通过单个操作员控制多个无人驾驶飞行器（UAV）的代表性网络中心界面的自动脱模系统。我们的概念被称为干旱，用于基于代理的信息密度降低。干旱假设是智能代理可以通过深入了解可以推断出对任务目标不太重要的显示器的那些方面来改善操作员性能。干旱代理商通过各种网络源提供的数据源收到有关世界的信息。每个代理都有了解用于表示各种实体，地形功能和区域的用户界面符号。代理商还提供了任务目标，用于推断给定符号与任务目标的成功的相关性。通过支持代理和分配BDU（信仰/难以置信/不确定性）值来计算诸如空间关系的第一级事实。动态规则集提供了推理机制，代理可以从给定的断言推断出新事实。我们开发了一种主观逻辑的证据推理网络，明确地在知识库中明确地处理了信仰和不确定性，并且用于从地图显示中的每个UI符号导出相关性信仰。主观逻辑用于组合值，当不同的源为相同符号提供不同的结果时组合值。用户界面代理应用相关性信仰的结果并转换显示器，以最小化由相关元素较少造成的表观杂物。两个转换，透明度和分组，用于当前实现。

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