A BDD-Based Algorithm for Learning from Interpretation Transition

Tony Ribeiro, Katsumi Inoue and Chiaki Sakama

Proceedings of the 23rd International Conference on Inductive Logic Programming (ILP), Lecture Notes in Artificial Intelligence 8812, Springer-Verlag, pages 47-63, 2013.


In recent years, there has been an extensive interest in learning the dynamics of systems. For this purpose, a previous work proposed an algorithm that learns a logic program from interpretation transitions. However, both the run time and the memory space of this algorithm alike are exponential. In this paper, we propose a new version of this algorithm utilizing an efficient data structure based on Zero-suppressed Binary Decision Diagrams. We show empirically that using this representation we can perform the same learning task faster and using less memory space.

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