Research on Learning Markov Logic Networks Based on GA and PSO

YU Peng; LIU Da-you; OUYANG Dan-tong

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Research on Learning Markov Logic Networks Based on GA and PSO

更新时间：2025-07-16

- Research on Learning Markov Logic Networks Based on GA and PSO
- Acta Electronica Sinica Vol. 34, Issue S1, Pages: 2551-2555(2006)
- 作者机构：
  
  吉林大学计算机科学与技术学院,吉林,长春,130012
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP183
- Published：2006
- 稿件说明：
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YU Peng, LIU Da-you, OUYANG Dan-tong. Research on Learning Markov Logic Networks Based on GA and PSO[J]. Acta Electronica Sinica, 2006, 34(S1): 2551-2555.
DOI：

YU Peng, LIU Da-you, OUYANG Dan-tong. Research on Learning Markov Logic Networks Based on GA and PSO[J]. Acta Electronica Sinica, 2006, 34(S1): 2551-2555. DOI：

摘要

Markov逻辑网(MLN)是一种重要的统计关系模型

目前其学习问题主要采用确定性的优化方法

所求的解不够简洁、易陷入局部极值.针对这些问题

本文定义谓词模板、子句模板以缩小搜索空间

提出一种采用子句模板编码的遗传算法来学习MLN结构

并用粒子群算法学习MLN的权参数.文中设计了适应度函数和相应的遗传算子

保证算法不断向好的逻辑子句结构进化.理论分析与实验结果都表明本文的算法可以学习到较优解.

Abstract

Markov Logic Networks(MLN) is an important model in Statistical Relational Learning.Nowadays deterministic search methods are the main methods to learn MLNs.However

the result was not compact and the algorithms can easily get into the local optima.Aiming at solving these problems

we defined the predicate template and the clause template to reduce search space and put forward a learning algorithm using Genetic Algorithm(GA)

which code is clause template

to learn MLNs's structure

using Particle Swarm Optimization(PSO) to learn MLN's weight.We gave the fitness function which makes the algorithm convergence

design the genetic operators.We testify our algorithm can get better result by theoretical analysis and experiment comparison.

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Related Institution

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