A BDD-Based algorithm for learning from interpretation transition

Abstract

In recent years, there has been an extensive interest in learning the dynamics of systems. For this purpose, a new learning method called learning from interpretation transition has been proposed recently [1]. However, both the run time and the memory space of this algorithm are exponential, so a better data structure and an efficient algorithm have been awaited. In this paper, we propose a new learning algorithm of this method utilizing an efficient data structure inspired from Ordered Binary Decision Diagrams. We show empirically that using this representation we can perform the same learning task faster with less memory space.