MCLA: Research on cumulative learning of Markov Logic Network

Abstract

Compared with other probabilistic semantic learning algorithms, Markov Logic Network (MLN) learning can integrate existing knowledge fragments. However, the knowledge fragments increase as the MLN learning algorithm traverses candidate objects, making the execution time of the MLN learning algorithm long. To shorten the execution time, an MLN Cumulative Learning Algorithm (MCLA) that combines the MLN learning algorithm and cumulative learning is proposed. MCLA can unify the existing knowledge and the new–old knowledge, perform multi-task learning under appropriate circumstances, and directly call the existing knowledge to learn the new knowledge. This paper applies MCLA to single-resident indoor activity scenarios to verify the effectiveness of the learning algorithm. Experiments have proved that MCLA not only ensures the accuracy and improves the versatility of knowledge, but also dramatically decreases the learning time and effectively manages the knowledge memory of the MLN.