摘要翻译：
我们检验了用户使用答案集编程(ASP)来表示逻辑形式化的实用性。我们选择一个从因果信息中捕捉因果解释的形式主义作为例子。我们提供了一个实现，显示了该翻译工作的自然性和相对效率。我们对编写ASP程序的简单性感兴趣，这与声称的ASP的“声明性”方面一致。早期系统的局限性（糟糕的数据结构和重用程序片段的困难）使得在实践中，“声明性方面”更多的是理论上的，而不是实际的。我们展示了在工作的ASP系统中最近的改进是如何促进翻译的，即使一些改进仍然是有用的。
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英文标题：
《Using ASP with recent extensions for causal explanations》
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作者：
Yves Moinard (INRIA - IRISA)
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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：
  We examine the practicality for a user of using Answer Set Programming (ASP) for representing logical formalisms. We choose as an example a formalism aiming at capturing causal explanations from causal information. We provide an implementation, showing the naturalness and relative efficiency of this translation job. We are interested in the ease for writing an ASP program, in accordance with the claimed ``declarative'' aspect of ASP. Limitations of the earlier systems (poor data structure and difficulty in reusing pieces of programs) made that in practice, the ``declarative aspect'' was more theoretical than practical. We show how recent improvements in working ASP systems facilitate a lot the translation, even if a few improvements could still be useful.
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PDF链接：
https://arxiv.org/pdf/1012.0830