Learning by switching generation and reasoning methods — acquisition of meta-knowledge for switching with reinforcement learning
Masahiro Tomaru, Motohide Umano, Yūji Matsumoto, Kazuhisa Seta · 2008
When we generate knowledge, we initially have no knowledge and acquire it by observing data one by one. We memorize the raw data when the number of observed data is small and generate general knowledge when it becomes large. To simulate this learning process, we proposed a learning model with switching several knowledge representation and reasoning methods. In this model, the time when to switch is decided with the fixed rules. These rules are considered to be meta-knowledge because they control the learning process. In this paper, we propose a method acquiring the meta-knowledge for deciding the time of switching knowledge representation or reasoning method. For learning of the meta-knowledge, the correct answers can not to be given but just the evaluation of the learning process. We use Q-learning, therefore, a method of reinforcement learning. In the simulation, we apply the method to the iris plant data to acquire the meta-knowledge. The system with the acquired meta-knowledge has smaller number of rules than the old method for the similar rate correctly classified.