MCLA: Research on cumulative learning of Markov Logic Network

作者：

Highlights：

• The increase of knowledge reserve in the Markov network reduces the MLN learning efficiency.

• MCLA improves learning efficiency and knowledge storage by combining MLN learning and cumulative learning.

• Creating a knowledge list K in MCLA improves the versatility of knowledge and enables multi-task learning.

摘要

•The increase of knowledge reserve in the Markov network reduces the MLN learning efficiency.•MCLA improves learning efficiency and knowledge storage by combining MLN learning and cumulative learning.•Creating a knowledge list K in MCLA improves the versatility of knowledge and enables multi-task learning.

论文关键词：Markov Logic Network,Cumulative learning,Unification of new–old knowledge,Activity recognition,Versatility

论文评审过程：Received 2 June 2021, Revised 30 December 2021, Accepted 29 January 2022, Available online 4 February 2022, Version of Record 16 February 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.108352