Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 3
805-810

Knowledge reasoning method based on hierarchical reinforcement learning

Sun Chonga
Wang Haironga,b
Jing Boxianga
Ma Hea
a. College of Computer Science & Engineering, b. The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0309

Abstract

In the process of knowledge inference, with the increase of the length of the inference path, the action space of the node increases sharply, which makes the inference difficulty continue to increase. This paper proposed a knowledge reasoning method of hierarchical reinforcement learning(MutiAg-HRL) to reduce the size of action space in the reasoning process. MutiAg-HRL invoked high-level agents to perform rough reasoning on the relationships in the knowledge graph, and determined the approximate location of the target entity by calculating the similarity between the next step relationship and the given query relationship. According to the relationship given by the high-level agent, the low-level agents were guided to conduct detailed reasoning and select the next action. The model also constructed an interactive reward mechanism to reward the relationship between the two agents and the choice of actions in time to prevent the problem of sparse reward in the model. To verify the effectiveness of the proposed method, it carried out experiments on FB15K-237 and NELL-995 datasets. The experimental results were compared with those of 11 mainstream methods such as TransE, MINERVA and HRL. The average value of the MutiAg-HRL method on the link prediction task hits@k is increased by 1.85%, MRR increases by an average of 2%.

Foundation Support

宁夏自然科学基金资助项目(2023AAC03316)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0309
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 3
Section: Algorithm Research & Explore
Pages: 805-810
Serial Number: 1001-3695(2024)03-023-0805-06

Publish History

[2023-11-17] Accepted Paper
[2024-03-05] Printed Article

Cite This Article

孙崇, 王海荣, 荆博祥, 等. 一种分层强化学习的知识推理方法 [J]. 计算机应用研究, 2024, 41 (3): 805-810. (Sun Chong, Wang Hairong, Jing Boxiang, et al. Knowledge reasoning method based on hierarchical reinforcement learning [J]. Application Research of Computers, 2024, 41 (3): 805-810. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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