摘要翻译:
为了给“如果A那么通常B”这种形式的定性条件赋予适当的语义,可以使用序数条件函数根据可能世界的似然程度对其进行排序。一个接受知识库R所有条件的OCF可以刻画为一个约束满足问题的解。我们提出了一种使用约束逻辑编程技术来解决约束满足问题的高级声明性方法。特别是,这里开发的方法支持生成所有最小解;这些最小解具有特殊的意义,因为它们为基于模型的推理提供了基础。
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英文标题:
《A Constraint Logic Programming Approach for Computing Ordinal
Conditional Functions》
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作者:
Christoph Beierle, Gabriele Kern-Isberner, Karl S\"odler
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最新提交年份:
2011
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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:
In order to give appropriate semantics to qualitative conditionals of the form "if A then normally B", ordinal conditional functions (OCFs) ranking the possible worlds according to their degree of plausibility can be used. An OCF accepting all conditionals of a knowledge base R can be characterized as the solution of a constraint satisfaction problem. We present a high-level, declarative approach using constraint logic programming techniques for solving this constraint satisfaction problem. In particular, the approach developed here supports the generation of all minimal solutions; these minimal solutions are of special interest as they provide a basis for model-based inference from R.
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PDF链接:
https://arxiv.org/pdf/1108.5794