Parallel Learning for Combined Knowledge Acquisition Model

Abstract

In this paper, we propose a novel learning method for the combined knowledge acquisition model. The combined knowledge acquisition model is a model for knowledge acquisition in which an agent heuristically find new knowledge by integrating existing plural knowledge. In the conventional model, there are two separate phases for combined knowledge acquisition: (a) solving a task with existing knowledge by trial and error and (b) learning new knowledge based on the experience in solving the task. However, since these two phases are carried out serially, the efficiency of learning was poor. In this paper, in order to improve this problem, we propose a novel knowledge acquisition method which realizes two phases simultaneously. Computer simulation results show that the proposed method much improves the efficiency of learning new knowledge.