摘要翻译：
军事化冲突是对社会产生重大影响的风险之一。军事化国家间争端(MID)是国家间相互作用的结果，它可能导致和平或冲突。有效预测国家间冲突的可能性是决策者的重要决策支持工具。在以前的研究中，神经网络(NNs)已经实现了对MID的预测。支持向量机（SVMs）已被证明是一种非常好的预测技术，本文将其用于MIDs的预测，并与神经网络进行了比较。结果表明，支持向量机比神经网络更好地预测MID，而神经网络比支持向量机具有更好的一致性和更易于解释的敏感性分析。
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英文标题：
《Artificial Intelligence for Conflict Management》
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作者：
E. Habtemariam, T. Marwala and M. Lagazio
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最新提交年份：
2007
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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：
  Militarised conflict is one of the risks that have a significant impact on society. Militarised Interstate Dispute (MID) is defined as an outcome of interstate interactions, which result on either peace or conflict. Effective prediction of the possibility of conflict between states is an important decision support tool for policy makers. In a previous research, neural networks (NNs) have been implemented to predict the MID. Support Vector Machines (SVMs) have proven to be very good prediction techniques and are introduced for the prediction of MIDs in this study and compared to neural networks. The results show that SVMs predict MID better than NNs while NNs give more consistent and easy to interpret sensitivity analysis than SVMs.
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PDF链接：
https://arxiv.org/pdf/0705.1209