Probabilistic Inductive Logic Programming Based on Answer Set Programming
Probabilistic Inductive Logic Programming Based on Answer Set Programming
复制标题
基于答案集规划的概率归纳逻辑规划
DOI：
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复制
发表时间：
2014
期刊：
影响因子：
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通讯作者：
A. Mileo
中科院分区：
文献类型：
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作者：
Matthias Nickles;A. Mileo
我们提出了一种新的形式化语言的基础上的答案集编程（ASP）的概率知识的表达表示。它允许注释一阶公式以及ASP规则和具有概率的事实，并从数据中学习这些权重（参数估计）。加权公式给出了一个语义方面的软，硬约束，决定了概率分布的答案集。在相关的方法相比，我们的方法推理可选地利用所谓的精简XOR约束，以减少计算的答案集的数量。我们的方法是原型实现。举例说明了引入的概念，并指出了未来研究的问题和主题。
We propose a new formal language for the expressive representation of probabilistic knowledge based on Answer Set Programming (ASP). It allows for the annotation of first-order formulas as well as ASP rules and facts with probabilities and for learning of such weights from data (parameter estimation). Weighted formulas are given a semantics in terms of soft and hard constraints which determine a probability distribution over answer sets. In contrast to related approaches, we approach inference by optionally utilizing so-called streamlining XOR constraints, in order to reduce the number of computed answer sets. Our approach is prototypically implemented. Examples illustrate the introduced concepts and point at issues and topics for future research.
DOI：
10.1093/jigpal/jzs010
发表时间：
2012
期刊：
Log. J. IGPL
影响因子：
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作者：
G. Kern-Isberner
通讯作者：
G. Kern-Isberner