The Learning Algorithm of Real-Time Automata

Traditional machine learning algorithms heavily depend on training data. In order to reduce the amount of training data, active learning is proposed to find out the critical data, which place more important roles against other data. The active learning algorithm is also used to learn real-time automaton(RTA). However, a huge number of membership queries and equivalence queries are generated in the learning process. In this paper, We design a new data structure to store the information obtained by membership queries. This data structure is a kind of tree structure, and improve the efficiency of the active learning for real-time RTA because this structure can process counter-examples effectively. Some experiments are conducted, and the results show that the algorithm can significantly reduce the number of membership queries without increasing the equivalence queries numbers. From the data point of view, our algorithm reduces the number of membership queries by 50% and the execution time by 80%.