摘要翻译：
针对多目标空中威胁，提出了一种基于两阶段柔性动态决策支持的最优威胁评估和防御资源调度算法。该算法通过在两个目标函数（即优先防御策略和减法防御策略）之间进行切换，提供了灵活性和最优性。为了进一步提高解决方案的质量，将威胁评估和武器分配(TEWA)中使用的关键参数归纳为三大类（触发参数、调度参数和排序参数）。该算法采用多对多稳定婚姻算法(SMA)的一种变体来解决威胁评估(TE)和武器分配(WA)问题。在TE阶段，进行威胁排序和威胁-资产配对。第二阶段基于一种新的灵活的动态武器调度算法，允许使用射-看-射策略进行多次交战，以计算一系列场景的近最优解。本文的分析部分介绍了在不同的离线场景下，该算法相对于另一种贪婪算法的优缺点。
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英文标题：
《A Novel Two-Stage Dynamic Decision Support based Optimal Threat
  Evaluation and Defensive Resource Scheduling Algorithm for Multi Air-borne
  threats》
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作者：
Huma Naeem, Asif Masood, Mukhtar Hussain, Shoab A. Khan
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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：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：
  This paper presents a novel two-stage flexible dynamic decision support based optimal threat evaluation and defensive resource scheduling algorithm for multi-target air-borne threats. The algorithm provides flexibility and optimality by swapping between two objective functions, i.e. the preferential and subtractive defense strategies as and when required. To further enhance the solution quality, it outlines and divides the critical parameters used in Threat Evaluation and Weapon Assignment (TEWA) into three broad categories (Triggering, Scheduling and Ranking parameters). Proposed algorithm uses a variant of many-to-many Stable Marriage Algorithm (SMA) to solve Threat Evaluation (TE) and Weapon Assignment (WA) problem. In TE stage, Threat Ranking and Threat-Asset pairing is done. Stage two is based on a new flexible dynamic weapon scheduling algorithm, allowing multiple engagements using shoot-look-shoot strategy, to compute near-optimal solution for a range of scenarios. Analysis part of this paper presents the strengths and weaknesses of the proposed algorithm over an alternative greedy algorithm as applied to different offline scenarios.
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PDF链接：
https://arxiv.org/pdf/0906.5038