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摘要翻译：
约束模型转换是约束规划系统中的一项重要任务。用户可理解的模型是在建模阶段定义的，但重写或调整它们是得到求解效率高的模型的必要条件。我们提出了一个新的体系结构来定义任何（建模或求解）语言之间的桥梁，并实现模型优化。该体系结构遵循模型驱动的方法，其中约束建模过程被视为一组模型转换。其中，一个有趣的特性是将转换定义为面向概念的规则，即基于模型元素的类型，其中类型被组织成一个称为元模型的层次结构。
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英文标题：
《Using ATL to define advanced and flexible constraint model
transformations》
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作者：
Raphael Chenouard (LINA), Laurent Granvilliers (LINA), Ricardo Soto
(LINA)
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最新提交年份：
2010
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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：
Transforming constraint models is an important task in re- cent constraint programming systems. User-understandable models are defined during the modeling phase but rewriting or tuning them is manda- tory to get solving-efficient models. We propose a new architecture al- lowing to define bridges between any (modeling or solver) languages and to implement model optimizations. This architecture follows a model- driven approach where the constraint modeling process is seen as a set of model transformations. Among others, an interesting feature is the def- inition of transformations as concept-oriented rules, i.e. based on types of model elements where the types are organized into a hierarchy called a metamodel.
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PDF链接：
https://arxiv.org/pdf/1002.3078
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