Functional Decomposition of MVL Functions Using Multi-Valued Decision Diagrams

In this paper, the minimization of incompletely specified multi-valued functions using functional decomposition is discussed. From the aspect of machine learning, learning samples can be implemented as minterms in multi-valued logic. The representation, can then be decomposed into smaller blocks, resulting in a reduced problem complexity. This gives induced descriptions through structuring, or feature extraction, of a learning problem. Our approach to the decomposition is based on expressing a multi-valued function (learning problem) in terms of a Multi-valued Decision Diagram that allows the use of Don’t Cares. The inclusion of Don’t Cares is the emphasis for this paper since multivalued benchmarks are characterized as having many Don’t Cares.