CHR(PRISM)-based probabilistic logic learning

JON SNEYERS; WANNES MEERT; JOOST VENNEKENS; YOSHITAKA KAMEYA; TAISUKE SATO

首页> 外文期刊>Theory and Practice of Logic Programming >CHR(PRISM)-based probabilistic logic learning

【24h】

CHR(PRISM)-based probabilistic logic learning

机译：基于CHR（PRISM）的概率逻辑学习

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

PRISM is an extension of Prolog with probabilistic predicates and built-in support for expectation-maximization learning. Constraint Handling Rules (CHR) is a high-level programming language based on multi-headed multiset rewrite rules.rnIn this paper, we introduce a new probabilistic logic formalism, called CHRiSM, based on a combination of CHR and PRISM. It can be used for high-level rapid prototyping of complex statistical models by means of "chance rules". The underlying PRISM system can then be used for several probabilistic inference tasks, including probability computation and parameter learning. We define the CHRiSM language in terms of syntax and operational semantics, and illustrate it with examples. We define the notion of ambiguous programs and define a distribution semantics for unambiguous programs. Next, we describe an implementation of CHRiSM, based on CHR(PRISM). We discuss the relation between CHRiSM and other probabilistic logic programming languages, in particular PCHR. Finally, we identify potential application domains.

机译：PRISM是Prolog的扩展，具有概率谓词和对期望最大化学习的内置支持。约束处理规则（Constraint Handling Rules，CHR）是一种基于多头多集重写规则的高级编程语言。在本文中，我们介绍了一种新的概率逻辑形式主义，称为CHRiSM，它是基于CHR和PRISM的组合。它可以通过“机会规则”用于复杂统计模型的高级快速原型制作。然后可以将基础PRISM系统用于几个概率推理任务，包括概率计算和参数学习。我们根据语法和操作语义定义CHRiSM语言，并通过示例进行说明。我们定义歧义程序的概念，并为歧义程序定义分布语义。接下来，我们描述基于CHR（PRISM）的CHRiSM的实现。我们讨论了CHRiSM与其他概率逻辑编程语言（尤其是PCHR）之间的关系。最后，我们确定潜在的应用领域。

著录项

来源
《Theory and Practice of Logic Programming》 |2010年第6期|P.433-447|共15页
作者
JON SNEYERS; WANNES MEERT; JOOST VENNEKENS; YOSHITAKA KAMEYA; TAISUKE SATO;
展开▼
作者单位

Department of Computer Science, K.U. Leuven, Belgium;

rnDepartment of Computer Science, K.U. Leuven, Belgium;

rnDepartment of Computer Science, K.U. Leuven, Belgium;

Tokyo Institute of Technology, Japan;

rnTokyo Institute of Technology, Japan;

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类
关键词
probabilistic logic learning; constraint handling rules;

机译：概率逻辑学习约束处理规则;

相似文献

外文文献
中文文献
专利

1. Small Lung Nodules Detection Based on Fuzzy-Logic and Probabilistic Neural Network With Bioinspired Reinforcement Learning [J] . Capizzi Giacomo, Lo Sciuto Grazia, Napoli Christian, IEEE Transactions on Fuzzy Systems . 2020,第6期

机译：基于模糊逻辑和概率神经网络的小肺结节检测与Bioinspired加强学习
2. Bandit-based Monte-Carlo structure learning of probabilistic logic programs [J] . Di Mauro Nicola, Bellodi Elena, Riguzzi Fabrizio Machine Learning . 2015,第1期

机译：基于Bandit的概率逻辑程序的蒙特卡洛结构学习
3. Learning probabilistic logic models from probabilistic examples [J] . Jianzhong Chen, Stephen Muggleton, Jose Santos Machine Learning . 2008,第1期

机译：从概率示例中学习概率逻辑模型
4. Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract) [C] . Jianzhong Chen, Stephen Muggleton, Jose Santos International Conference on Inductive Logic Programming;ILP 2007 . 2008

机译：从概率示例中学习概率逻辑模型（扩展摘要）
5. Comparison of graph-based and logic-based relational learning [D] . Ketkar, Nikhil 2004

机译：基于图和基于逻辑的关系学习的比较
6. Learning Probabilistic Logic Models from Probabilistic Examples [O] . Jianzhong Chen, Stephen Muggleton, José Santos -1

机译：从概率示例中学习概率逻辑模型
7. CHR(PRISM)-based probabilistic logic learning [O] . Sneyers Jon, Meert Wannes, Vennekens Joost, 2010

机译：基于CHR（PRISM）的概率逻辑学习
8. Creating Robust Relation Extract and Anomaly Detect via Probabilistic Logic-Based Reasoning and Learning. [R] . Shavlik, J., Re, C., Natarajan, S. 2017

机译：通过基于概率逻辑的推理和学习创建鲁棒关系提取和异常检测。

CHR(PRISM)-based probabilistic logic learning

摘要

著录项

相似文献

相关主题

期刊订阅