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摘要翻译：
输气管网是大型复杂系统，相应的设计和控制问题具有挑战性。本文考虑危机情况下这些系统的控制和管理问题。我们通过一个混合系统框架来描述这些网络，该框架提供了所需的分析模型。在此基础上，我们利用计算离散和混合优化方法讨论了决策问题。特别是，在特定的危机情况下，采用了几种强化学习方法来探索决策空间，实现最优策略。仿真结果表明了该方法的有效性。
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英文标题：
《Hybrid systems modeling for gas transmission network》
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作者：
Amir Noori, Mohammad Bagher Menhaj, Masoud Shafiee
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最新提交年份：
2012
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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：
Gas Transmission Networks are large-scale complex systems, and corresponding design and control problems are challenging. In this paper, we consider the problem of control and management of these systems in crisis situations. We present these networks by a hybrid systems framework that provides required analysis models. Further, we discuss decision-making using computational discrete and hybrid optimization methods. In particular, several reinforcement learning methods are employed to explore decision space and achieve the best policy in a specific crisis situation. Simulations are presented to illustrate the efficiency of the method.
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PDF链接：
https://arxiv.org/pdf/1208.1743
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