摘要翻译：
定性时间信息的表示和推理是许多人工智能任务的重要组成部分。文学界提出了许多模型来表示这种时间信息。所有这些都是从基于点或基于区间的框架中派生出来的。在这些框架的应用中，一个基本的推理任务由以下方案给出：给定一些时态对象之间的二元关系的可能不确定和不完全的知识，寻找所有这些对象之间的一致场景。所有这些模型都需要传递表--或者类似的推理规则--来解决这些任务。我们基于\emph{优先级}和\emph{同时性}这两个关系，定义了一个表示定性时间信息的替代模型&S语言。在本文中，我们展示了该模型如何避免传递表或推理规则来处理这类问题。
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英文标题：
《Temporal Reasoning without Transitive Tables》
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作者：
Sylviane R. Schwer (LIPN)
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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：
  Representing and reasoning about qualitative temporal information is an essential part of many artificial intelligence tasks. Lots of models have been proposed in the litterature for representing such temporal information. All derive from a point-based or an interval-based framework. One fundamental reasoning task that arises in applications of these frameworks is given by the following scheme: given possibly indefinite and incomplete knowledge of the binary relationships between some temporal objects, find the consistent scenarii between all these objects. All these models require transitive tables -- or similarly inference rules-- for solving such tasks. We have defined an alternative model, S-languages - to represent qualitative temporal information, based on the only two relations of \emph{precedence} and \emph{simultaneity}. In this paper, we show how this model enables to avoid transitive tables or inference rules to handle this kind of problem.
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PDF链接：
https://arxiv.org/pdf/0706.1290