摘要翻译：
能力规划问题普遍存在于人类感兴趣的许多领域，在国防和安全领域有突出的例子。规划为优化提供了一个独特的背景，但它还没有被详细地探索，并且涉及到许多有趣的挑战，这些挑战不同于传统的优化研究。规划问题要求解决方案能够在多个尺度上满足许多相互竞争的目标，涉及鲁棒性、适应性、风险性等。情景方法是规划的一种关键方法。可以为长期计划和短期计划定义方案。本文介绍了基于计算情景的规划问题，并提出了在战术规划域内适应战略定位的方法。在一个用多目标进化算法求解的资源规划问题中，我们演示了该方法。我们的讨论和结果强调了这样一个事实，即基于情景的规划自然是在一个多目标的环境中进行的。然而，相互冲突的目标发生在不同的系统级别上，而不是在单个系统内。本文还认为，规划问题在许多人类努力中是至关重要的，进化计算可能很好地定位于这个问题领域。
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英文标题：
《Strategic Positioning in Tactical Scenario Planning》
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作者：
James M. Whitacre, Hussein A. Abbass, Ruhul Sarker, Axel Bender,
  Stephen Baker
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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：
  Capability planning problems are pervasive throughout many areas of human interest with prominent examples found in defense and security. Planning provides a unique context for optimization that has not been explored in great detail and involves a number of interesting challenges which are distinct from traditional optimization research. Planning problems demand solutions that can satisfy a number of competing objectives on multiple scales related to robustness, adaptiveness, risk, etc. The scenario method is a key approach for planning. Scenarios can be defined for long-term as well as short-term plans. This paper introduces computational scenario-based planning problems and proposes ways to accommodate strategic positioning within the tactical planning domain. We demonstrate the methodology in a resource planning problem that is solved with a multi-objective evolutionary algorithm. Our discussion and results highlight the fact that scenario-based planning is naturally framed within a multi-objective setting. However, the conflicting objectives occur on different system levels rather than within a single system alone. This paper also contends that planning problems are of vital interest in many human endeavors and that Evolutionary Computation may be well positioned for this problem domain.
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PDF链接：
https://arxiv.org/pdf/0907.0340