We consider the use of ordered binary decision diagrams (OBDDs) as a means of realizing knowledge-bases, and show that, from the view point of space requirement, the OBDD-based representation is more efficient and suitable in some cases, compared with the traditional CNF-based and/or model-based representations. We then present polynomial time algorithms for the two problems of testing whether a given OBDD represents a unate Boolean function, and of testing whether it represents a Horn function. © 2002 Published by Elsevier Science B.V.
Horiyama, T., & Ibaraki, T. (2002). Ordered binary decision diagrams as knowledge-bases. Artificial Intelligence, 136(2), 189–213. https://doi.org/10.1016/S0004-3702(02)00119-4