未来舰队的鲁棒性和适应性分析

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摘要翻译：
对未来军事舰队的结构做出决定是一项具有挑战性的任务。需要考虑几个问题，例如存在多个相互竞争的目标和操作环境的复杂性。未来可能存在的各种不确定性构成了一个特别的挑战。不确定未来可能会遇到什么事件；舰队设计决策将如何影响和塑造未来；以及现在和未来的决策者将如何根据现有的信息、他们对不同目标的重要性的个人偏见以及他们的经济偏好采取行动。为了帮助战略决策，对未来机队选择的分析需要考虑这些不同类别的不确定性暴露的条件。重要的是要了解一个特定的机队对什么假设是稳健的，什么机队可以随时适应，以及什么条件给机队带来明显的风险。我们称之为对一支舰队战略定位的分析。本文介绍了如何利用计算机模拟来评估战略定位。我们的主要目的是引入一个框架，以捕捉信息，可以有用的决策者和定义概念的稳健性和适应性在未来的舰队设计的背景下。我们通过对一个空运机队的模拟研究来证明我们的概念框架。我们采用一种探索性的基于场景的方法来捕捉不确定性。每个情景都代表了不同未来条件、不同模型假设和不同经济偏好的抽样。然后根据对机队的鲁棒性、适应性和对不同情况的风险的影响，分析对机队的拟议变更。
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英文标题：
《Robustness and Adaptiveness Analysis of Future Fleets》
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作者：
Slawomir Wesolkowski, Michael Mazurek, James M. Whitacre, Axel Bender,
  Hussein Abbass
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Making decisions about the structure of a future military fleet is a challenging task. Several issues need to be considered such as the existence of multiple competing objectives and the complexity of the operating environment. A particular challenge is posed by the various types of uncertainty that the future might hold. It is uncertain what future events might be encountered; how fleet design decisions will influence and shape the future; and how present and future decision makers will act based on available information, their personal biases regarding the importance of different objectives, and their economic preferences. In order to assist strategic decision-making, an analysis of future fleet options needs to account for conditions in which these different classes of uncertainty are exposed. It is important to understand what assumptions a particular fleet is robust to, what the fleet can readily adapt to, and what conditions present clear risks to the fleet. We call this the analysis of a fleet's strategic positioning. This paper introduces how strategic positioning can be evaluated using computer simulations. Our main aim is to introduce a framework for capturing information that can be useful to a decision maker and for defining the concepts of robustness and adaptiveness in the context of future fleet design. We demonstrate our conceptual framework using simulation studies of an air transportation fleet. We capture uncertainty by employing an explorative scenario-based approach. Each scenario represents a sampling of different future conditions, different model assumptions, and different economic preferences. Proposed changes to a fleet are then analysed based on their influence on the fleet's robustness, adaptiveness, and risk to different scenarios.
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PDF链接：
https://arxiv.org/pdf/0907.0598

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关键词：鲁棒性 适应性 Presentation Intelligence Evolutionary conditions 舰队 不同 particular fleet